library('Seurat')
library('ggplot2')
Attaching SeuratObject
data_10X <- Read10X(data.dir = "/lustre/projects/Stembryo_Seq/data/sc_sumit/GSE122187_WT_E8.0_1ab")
Emb8 <- CreateSeuratObject(counts=data_10X, project="Emb8", min.cells=3, min.features=200)
Emb8[["percent.mt"]] <- PercentageFeatureSet(Emb8, pattern = "^MT-")
Emb8 <- subset(Emb8, subset = nFeature_RNA > 3000 & nCount_RNA > 10000 & nCount_RNA < 80000 & percent.mt < 5)
Emb8 <- NormalizeData(Emb8, verbose = TRUE)
Emb8 <- FindVariableFeatures(Emb8, verbose = TRUE)
Emb8 <- ScaleData(Emb8, verbose = TRUE)
Emb8 <- RunPCA(Emb8, features = VariableFeatures(object = Emb8), verbose = TRUE)
Emb8 <- FindNeighbors(Emb8, dims = 1:20)
Emb8 <- FindClusters(Emb8, resolution = 0.5)
Emb8 <- RunUMAP(Emb8, dims = 1:20)
Centering and scaling data matrix PC_ 1 Positive: Fabp5, Tmsb10, Tuba1a, Sox11, Mdk, Cd24a, Cnn3, Hmga2, Ccnd2, Prtg Sox4, Prdx2, Ccnd1, Ptn, Snrpn, Ifitm1, Gm8730, Cadm1, Crabp1, Marcks Nnat, Map1b, Sfrp1, Fzd2, Mest, Ndn, Fam64a, Meis2, Tubb2b, Stmn2 Negative: Apom, Cubn, Amn, Lgals2, Ttr, Rbp4, Apob, Apoa1, Folr1, S100g Apoa4, Clic6, Apoa2, Tdh, Ctsh, Afp, Lgmn, Slc13a4, Spink1, Slc2a2 Car7, Pdzk1, 2610528J11Rik, 2210011C24Rik, Slc39a5, Atp1b1, Fmr1nb, 4933402E13Rik, S100a1, Apoc1 PC_ 2 Positive: Hbb-bh1, Gypa, Hba-x, Cited4, Hba-a1, Klf1, Snca, Blvrb, Hba-a2, Alas2 Gata1, Slc38a5, Trim10, Chchd10, Tspan33, Slc25a37, Car2, Cpne7, Mgst3, Cpox Acp5, Epor, Smim1, Ube2l6, Prdx2, Kcnn4, Ell2, Pnpo, Mrap, Glrx5 Negative: Serpinh1, Cnn2, Vim, Csrp2, Tpm4, Cald1, Tpm1, Myl6, Csrp1, Tmsb10 Acta2, Pmp22, Mest, Tagln, Clic1, Nrp1, Pdia6, Sparc, Anxa2, Igf2 Anxa6, Myl12a, Ppic, Actg1, Peg3, Fermt2, Ngfrap1, Wbp5, Rbpms, Tuba1a PC_ 3 Positive: Prtg, Mdk, Id3, Rbp1, Nr6a1, Sox11, Gja1, Id1, Wbp5, Crabp2 Gpc3, Ldhb, Cd24a, Slc9a3r1, Wls, Ptn, Emb, Id2, Actg1, Cadm1 Sox4, Car14, Slc2a3, Stmn2, Tubb2b, Ifitm1, Fzd2, Dnmt3b, Tspan13, Spint2 Negative: Tal1, Cited4, Blvrb, Gypa, Klf1, Lmo2, Hbb-bh1, Hba-a1, Gypc, Hba-x Snca, Lyl1, Alas2, Gata1, Hba-a2, Smim1, Car2, Tmem14c, Slc38a5, Epor Trim10, Gpx1, Prkar2b, Tspan32, Alad, Cpox, Slc43a3, Ccnd3, Slc25a37, Fermt3 PC_ 4 Positive: Rasip1, Icam2, Plvap, Ramp2, Cldn5, Esam, Ctla2a, Cdh5, Gngt2, Cd34 Rhoj, Grap, Gpr182, Fli1, Tie1, Ctla2b, Igf1, Vamp5, Flt4, Abi3 Eng, Elk3, Gimap1, Cd93, Arap3, Erg, Sox18, Pecam1, Scarf1, Ecscr Negative: Tnni1, Tnnt2, Actc1, Tnnc1, Myl4, Nebl, Ttn, Myl7, Nexn, Csrp3 Unc45b, Sh3bgr, Acta2, Popdc2, Tmod1, Hspb2, Ankrd1, Mybpc3, Cryab, Fbxl22 RP23-455C13.7, Cnn1, Ccdc141, Myh6, Smyd1, Hspb7, Myh7, Myl3, Nkx2-5, Asb2 PC_ 5 Positive: Fth1, Mdk, Sepp1, Bex4, Bex2, Rasgrp2, Npl, Igfbp4, Apoa1, Gpc3 Dab2, Apob, Ctsh, Apom, Rbp4, Cubn, Ttr, Lgals2, Clic6, Apoa2 Apoa4, Slc13a4, Gpx3, Car7, Tdh, Slc2a2, Mttp, Afp, S100g, Amn Negative: Gjb3, Gm9, Tex19.1, Utf1, Gjb5, Ascl2, Tfap2c, Sfn, Elf5, Fabp3 Ldoc1, Fthl17a, Tmem54, Dnmt3l, Sfmbt2, AU018091, Cldn3, Zfp42, S100a6, Pramef12 Rhox6, Hspb1, Fgfbp1, Wnt7b, Bmp8b, Rhox9, Ndrg1, Lgals3, Krt19, AU015836 Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 20545 Number of edges: 782830 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.9433 Number of communities: 22 Elapsed time: 3 seconds
Warning message: “The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session” 06:34:49 UMAP embedding parameters a = 0.9922 b = 1.112 06:34:49 Read 20545 rows and found 20 numeric columns 06:34:49 Using Annoy for neighbor search, n_neighbors = 30 06:34:49 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 06:34:53 Writing NN index file to temp file /tmp/Rtmpb4TuY7/file221a46e7df28a 06:34:53 Searching Annoy index using 1 thread, search_k = 3000 06:35:00 Annoy recall = 100% 06:35:00 Commencing smooth kNN distance calibration using 1 thread 06:35:02 Initializing from normalized Laplacian + noise 06:35:03 Commencing optimization for 200 epochs, with 882488 positive edges 06:35:32 Optimization finished
options(repr.plot.width = 10, repr.plot.height = 6)
VlnPlot(Emb8, features = ("nCount_RNA")) + geom_boxplot(width=0.1, fill="white")
VlnPlot(Emb8, features = ("nFeature_RNA")) + geom_boxplot(width=0.1, fill="white")
VlnPlot(Emb8, features = ("percent.mt")) + geom_boxplot(width=0.1, fill="white")
Warning message in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, : “All cells have the same value of percent.mt.”
Emb8 <- subset(Emb8, idents = 12, invert = TRUE)
#Emb8 <- subset(Emb8, idents = 17, invert = TRUE)
#Emb8 <- subset(Emb8, idents = 19, invert = TRUE)
Emb8 <- NormalizeData(Emb8, verbose = TRUE)
Emb8 <- FindVariableFeatures(Emb8, verbose = TRUE)
Emb8 <- ScaleData(Emb8, verbose = TRUE)
Emb8 <- RunPCA(Emb8, features = VariableFeatures(object = Emb8), verbose = TRUE)
Emb8 <- FindNeighbors(Emb8, dims = 1:20)
Emb8 <- FindClusters(Emb8, resolution = 0.5)
Emb8 <- RunUMAP(Emb8, dims = 1:20)
Centering and scaling data matrix PC_ 1 Positive: Apom, Cubn, Lgals2, Amn, Ttr, Rbp4, Apob, Apoa1, S100g, Apoa4 Clic6, Ctsh, Apoa2, Folr1, Tdh, Rhox5, Afp, Spink1, Lgmn, Slc13a4 Slc2a2, Car7, Pdzk1, 2210011C24Rik, Slc39a5, Atp1b1, Dab2, 2610528J11Rik, Fmr1nb, 4933402E13Rik Negative: Fabp5, Tmsb10, Sox11, Tuba1a, Mdk, Cd24a, Prdx2, Ccnd2, Cnn3, Hmga2 Prtg, Ptn, Gm8730, Sox4, Snrpn, Ccnd1, Cadm1, Crabp1, Ifitm1, Sfrp1 Fam64a, Stmn2, Nnat, Fzd2, Map1b, Marcks, Tubb2b, Meis2, Pou5f1, Gsta4 PC_ 2 Positive: Hbb-bh1, Hba-x, Gypa, Cited4, Hba-a1, Klf1, Snca, Blvrb, Chchd10, Hba-a2 Alas2, Gata1, Mgst3, Slc38a5, Trim10, Tspan33, Slc25a37, Car2, Acp5, Cpox Prdx2, Cpne7, Smim1, Glrx5, Epor, Ube2l6, Pnpo, F10, Kcnn4, Ell2 Negative: Serpinh1, Cnn2, Vim, Tpm4, Csrp2, Cald1, Tmsb10, Tpm1, Csrp1, Myl6 Mest, Pmp22, Acta2, Anxa2, Tagln, Myl12a, Nrp1, Pdia6, Tuba1a, Tubb6 Ngfrap1, Igf2, Ppic, Clic1, Actg1, Anxa6, Fermt2, Sparc, Pdlim7, Peg3 PC_ 3 Positive: Prtg, Mdk, Id3, Rbp1, Sox11, Nr6a1, Wbp5, Id1, Gja1, Id2 Cd24a, Crabp2, Ldhb, Gpc3, Slc9a3r1, Wls, Actg1, Ptn, Emb, Cadm1 Bex1, Dnmt3b, Sox4, Spint2, Tubb2b, Slc2a3, Ngfrap1, Fzd2, Stmn2, Ifitm1 Negative: Tal1, Lmo2, Cited4, Blvrb, Gypa, Hbb-bh1, Klf1, Gypc, Hba-a1, Hba-x Snca, Lyl1, Alas2, Hba-a2, Gata1, Smim1, Car2, Slc38a5, Tmem14c, Epor Prkar2b, Trim10, Gpx1, Alad, Tspan32, Cpox, Slc43a3, Pecam1, Slc25a37, Fermt3 PC_ 4 Positive: Tnni1, Tnnt2, Actc1, Myl4, Nebl, Tnnc1, Ttn, Myl7, Nexn, Csrp3 Unc45b, Sh3bgr, Acta2, Tmod1, Popdc2, Hspb2, Mybpc3, Ankrd1, Cryab, Fbxl22 RP23-455C13.7, Myh6, Cnn1, Smyd1, Ccdc141, Hspb7, Myh7, Myl3, Asb2, Nkx2-5 Negative: Rasip1, Ramp2, Icam2, Plvap, Cldn5, Esam, Ctla2a, Gngt2, Cdh5, Cd34 Rhoj, Gpr182, Grap, Tie1, Fli1, Igf1, Ctla2b, Vamp5, Flt4, Abi3 Eng, Elk3, Gimap1, Cd93, Arap3, Erg, Sox18, Scarf1, Ecscr, Flt1 PC_ 5 Positive: Ttn, Myl4, Ankrd1, Myh6, Unc45b, Myl3, Myh7, RP23-455C13.7, Fbxl22, Myl7 Hspb7, Csrp3, Asb2, Mef2c, Mybpc3, Smyd1, Apobec2, Nkx2-5, Tnnc1, Rcsd1 Ppp1r14c, Pgam2, Thbs4, Tnnt2, Cap2, Actc1, Nebl, Cnn1, Ldb3, Actn2 Negative: Hand1, Pmp22, Dlk1, Ahnak, Bmp4, Plac8, Col1a1, Pitx1, Spin2c, Krt8 Foxf1, Ccdc80, S100a10, Phlda2, Krt18, Snai2, Pcolce, Col1a2, Lgals1, Postn Wisp1, Slc38a4, Sdpr, Anxa2, Twist2, Ppp1r14a, Creb3l1, Basp1, Steap1, Cmtm7 Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 19760 Number of edges: 732651 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.9390 Number of communities: 22 Elapsed time: 2 seconds
06:42:47 UMAP embedding parameters a = 0.9922 b = 1.112 06:42:47 Read 19760 rows and found 20 numeric columns 06:42:47 Using Annoy for neighbor search, n_neighbors = 30 06:42:47 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 06:42:51 Writing NN index file to temp file /tmp/Rtmpb4TuY7/file221a454d3a503 06:42:51 Searching Annoy index using 1 thread, search_k = 3000 06:42:58 Annoy recall = 100% 06:42:58 Commencing smooth kNN distance calibration using 1 thread 06:43:00 Initializing from normalized Laplacian + noise 06:43:01 Commencing optimization for 200 epochs, with 840548 positive edges 06:43:29 Optimization finished
VlnPlot(Emb8, features = ("nCount_RNA")) + geom_boxplot(width=0.1, fill="white")
VlnPlot(Emb8, features = ("nFeature_RNA")) + geom_boxplot(width=0.1, fill="white")
#maybe need to filter more?
#17, 19 are sus
#maybe do a cluster diff exp analysis
emb8 <- Emb8
emb8
Emb8
An object of class Seurat 20210 features across 19760 samples within 1 assay Active assay: RNA (20210 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap
An object of class Seurat 20210 features across 19760 samples within 1 assay Active assay: RNA (20210 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap
DimPlot(Emb8, reduction = "umap", label = TRUE)
cluster0 <- FindMarkers(Emb8, ident.1 = 0, min.pct = 0.25, only.pos = TRUE)
cluster1 <- FindMarkers(Emb8, ident.1 = 1, min.pct = 0.25, only.pos = TRUE)
cluster2 <- FindMarkers(Emb8, ident.1 = 2, min.pct = 0.25, only.pos = TRUE)
cluster3 <- FindMarkers(Emb8, ident.1 = 3, min.pct = 0.25, only.pos = TRUE)
cluster4 <- FindMarkers(Emb8, ident.1 = 4, min.pct = 0.25, only.pos = TRUE)
cluster5 <- FindMarkers(Emb8, ident.1 = 5, min.pct = 0.25, only.pos = TRUE)
cluster6 <- FindMarkers(Emb8, ident.1 = 6, min.pct = 0.25, only.pos = TRUE)
cluster7 <- FindMarkers(Emb8, ident.1 = 7, min.pct = 0.25, only.pos = TRUE)
cluster8 <- FindMarkers(Emb8, ident.1 = 8, min.pct = 0.25, only.pos = TRUE)
cluster9 <- FindMarkers(Emb8, ident.1 = 9, min.pct = 0.25, only.pos = TRUE)
cluster10 <- FindMarkers(Emb8, ident.1 = 10, min.pct = 0.25, only.pos = TRUE)
cluster11 <- FindMarkers(Emb8, ident.1 = 11, min.pct = 0.25, only.pos = TRUE)
cluster12 <- FindMarkers(Emb8, ident.1 = 12, min.pct = 0.25, only.pos = TRUE)
cluster13 <- FindMarkers(Emb8, ident.1 = 13, min.pct = 0.25, only.pos = TRUE)
cluster14 <- FindMarkers(Emb8, ident.1 = 14, min.pct = 0.25, only.pos = TRUE)
cluster15 <- FindMarkers(Emb8, ident.1 = 15, min.pct = 0.25, only.pos = TRUE)
cluster16 <- FindMarkers(Emb8, ident.1 = 16, min.pct = 0.25, only.pos = TRUE)
cluster17 <- FindMarkers(Emb8, ident.1 = 17, min.pct = 0.25, only.pos = TRUE)
cluster18 <- FindMarkers(Emb8, ident.1 = 18, min.pct = 0.25, only.pos = TRUE)
cluster19 <- FindMarkers(Emb8, ident.1 = 19, min.pct = 0.25, only.pos = TRUE)
cluster20 <- FindMarkers(Emb8, ident.1 = 20, min.pct = 0.25, only.pos = TRUE)
cluster21 <- FindMarkers(Emb8, ident.1 = 21, min.pct = 0.25, only.pos = TRUE)
clus0 <- cluster0[,0]
clus1 <- cluster1[,0]
clus2 <- cluster2[,0]
clus3 <- cluster3[,0]
clus4 <- cluster4[,0]
clus5 <- cluster5[,0]
clus6 <- cluster6[,0]
clus7 <- cluster7[,0]
clus8 <- cluster8[,0]
clus9 <- cluster9[,0]
clus10 <- cluster10[,0]
clus11 <- cluster11[,0]
clus12 <- cluster12[,0]
clus13 <- cluster13[,0]
clus14 <- cluster14[,0]
clus15 <- cluster15[,0]
clus16 <- cluster16[,0]
clus17 <- cluster17[,0]
clus18 <- cluster18[,0]
clus19 <- cluster19[,0]
clus20 <- cluster20[,0]
clus21 <- cluster21[,0]
clus0 <- head(clus0, n=10)
clus1 <- head(clus1, n=10)
clus2 <- head(clus2, n=10)
clus3 <- head(clus3, n=10)
clus4 <- head(clus4, n=10)
clus5 <- head(clus5, n=10)
clus6 <- head(clus6, n=10)
clus7 <- head(clus7, n=10)
clus8 <- head(clus8, n=10)
clus9 <- head(clus9, n=10)
clus10 <- head(clus10, n=10)
clus11 <- head(clus11, n=10)
clus12 <- head(clus12, n=10)
clus13 <- head(clus13, n=10)
clus14 <- head(clus14, n=10)
clus15 <- head(clus15, n=10)
clus16 <- head(clus16, n=10)
clus17 <- head(clus17, n=10)
clus18 <- head(clus18, n=10)
clus19 <- head(clus19, n=10)
clus20 <- head(clus20, n=10)
clus21 <- head(clus21, n=10)
clus0
clus1
clus2
clus3
clus4
clus5
clus6
clus7
clus8
clus9
clus10
clus11
clus12
clus13
clus14
clus15
clus16
clus17
clus18
clus19
clus20
clus21
| Hoxd9 |
|---|
| Hoxd4 |
| Hoxd1 |
| Gpc3 |
| Cited1 |
| Cdx4 |
| Pcdh19 |
| Lef1 |
| Msx1 |
| Naaa |
| Cox5b |
|---|
| Mrpl30 |
| Hspd1 |
| Hspe1 |
| Abcb6 |
| Tuba4a |
| Steap3 |
| Prdx6 |
| Creg1 |
| Mgst3 |
| Pax3 |
|---|
| En1 |
| Pcsk1n |
| Stmn2 |
| Fabp5 |
| Sox2 |
| Cntfr |
| Pax5 |
| Pou3f1 |
| Mn1 |
| Itih5 |
|---|
| Prrx2 |
| Tcf15 |
| Nnat |
| Cer1 |
| Foxd2 |
| Rspo1 |
| Nbl1 |
| Mfap2 |
| Col26a1 |
| Cfc1 |
|---|
| Lefty2 |
| Tgfb2 |
| Prrx2 |
| Aplnr |
| Gata5 |
| Runx1t1 |
| Smarcd3 |
| Wnt2 |
| Peg3 |
| Rxrg |
|---|
| Set |
| Nr6a1 |
| Sp5 |
| Cdx4 |
| Slc16a2 |
| Stmn2 |
| Psip1 |
| Tcea3 |
| Acot7 |
| Pax6 |
|---|
| Porcn |
| Slc7a3 |
| Sox2 |
| Crabp2 |
| Pcsk9 |
| Hes3 |
| Hes5 |
| Ptn |
| Hoxa1 |
| Fzd5 |
|---|
| Igfbp2 |
| Lhx2 |
| Ermn |
| Pax6 |
| Pim2 |
| Pcsk1n |
| Eras |
| Hdac6 |
| Pls3 |
| Sox17 |
|---|
| Ptpn18 |
| Klf7 |
| Fn1 |
| Ralb |
| Rcsd1 |
| Itpkb |
| Atf3 |
| Cd34 |
| Vim |
| Sgk3 |
|---|
| Slc40a1 |
| 1700019D03Rik |
| Pgap1 |
| Mreg |
| Vil1 |
| Ihh |
| Tuba4a |
| 2810459M11Rik |
| Spp2 |
| Twist2 |
|---|
| Rgs5 |
| Vim |
| Prrx2 |
| Rbms1 |
| Hoxd9 |
| Cox4i2 |
| Snai1 |
| Gata5 |
| Suv39h1 |
| Cacna1b |
|---|
| Fermt1 |
| Wfdc2 |
| Eya2 |
| Rab25 |
| Npnt |
| Esrp1 |
| Acer2 |
| Gjb3 |
| Tmem54 |
| Sox17 |
|---|
| Ihh |
| Fermt1 |
| Foxa2 |
| Rab25 |
| Slc16a1 |
| Shh |
| Cldn4 |
| Trh |
| Foxa3 |
| Wnt6 |
|---|
| Lad1 |
| Batf3 |
| Spint1 |
| Fermt1 |
| Rab25 |
| Npnt |
| Esrp1 |
| Gjb3 |
| Tmem54 |
| Col3a1 |
|---|
| Col5a2 |
| Sdpr |
| Ramp1 |
| Hlx |
| Ptges |
| Cobll1 |
| Creb3l1 |
| Fam65c |
| Mid1ip1 |
| Col3a1 |
|---|
| Col5a2 |
| Sdpr |
| Ackr3 |
| Rgs5 |
| Sertad4 |
| Cobll1 |
| Itga4 |
| Creb3l1 |
| Lrrn4 |
| Myl1 |
|---|
| Des |
| Sphkap |
| Ramp1 |
| Rassf5 |
| Lmod1 |
| Tnni1 |
| Tnnt2 |
| Rcsd1 |
| Dusp27 |
| Gata1 |
|---|
| Alas2 |
| Cited4 |
| Snca |
| Gypa |
| Klf1 |
| Trim10 |
| Mrap |
| Slc38a5 |
| Hba-a2 |
| Elf3 |
|---|
| Fam129a |
| Fasl |
| Sfmbt2 |
| Gsn |
| Cd82 |
| Elf5 |
| AA467197 |
| Gtsf1l |
| Tfap2c |
| Avpr1b |
|---|
| Lmx1a |
| Atp1a2 |
| Morn5 |
| Pifo |
| Gm694 |
| Shh |
| 9530036O11Rik |
| Gm26894 |
| Mnx1 |
| Grem2 |
|---|
| Thbd |
| Pmepa1 |
| Ptk6 |
| Drp2 |
| Ptger3 |
| Lrrc38 |
| Hs3st1 |
| Eva1a |
| Fxyd3 |
| Prdm14 |
|---|
| Gdf3 |
| Dppa3 |
| Nanos3 |
| Psmb8 |
| Sox15 |
| Ceacam10 |
| Slpi |
| Lrrc34 |
| Isl2 |
FeaturePlot(Emb8, features = ('Meox1'))
meoxg <- subset(x = Emb8, subset = Meox1 > 1)
meoxg <- NormalizeData(meoxg, verbose = TRUE)
meoxg <- FindVariableFeatures(meoxg, verbose = TRUE)
meoxg <- ScaleData(meoxg, verbose = TRUE)
meoxg <- RunPCA(meoxg, features = VariableFeatures(meoxg), verbose = TRUE)
meoxg <- FindNeighbors(meoxg, dims = 1:20)
meoxg <- FindClusters(meoxg, resolution = 0.2)
meoxg <- RunUMAP(meoxg, dims = 1:20)
Centering and scaling data matrix PC_ 1 Positive: Pcdh19, Dll3, Ifitm1, Lef1, Hoxb1, Cited1, Dll1, Laptm4b, Aldh1a2, Notch1 Rspo3, Aldoc, Cdx1, Magi1, Tbx6, Hes7, Hoxb4, Hoxd4, Slc9a3r1, Pcdh8 Gpc3, Nrarp, Tubb2b, Gstm5, Fgf18, Rbp1, Mllt3, Hoxb5os, Synm, Mesp1 Negative: Apoe, Mest, Vamp8, Slc25a4, Csrp2, Hapln1, Eva1b, Fst, Marcks, Irx3 Cdh11, Eno3, Ldha, Flrt2, Prrx2, Tbx1, Hsd11b2, Col18a1, H19, Wdr86 Igfbp5, Peg3, Prrx1, Galk1, Hdgf, Rbbp7, Fth1, Tmem119, Cyr61, Dab2 PC_ 2 Positive: Meox1, Tcf15, Fst, Arg1, Foxc2, Marcks, Fbn2, Nnat, Ptn, Itih5 Anxa5, Glrx, Tbx18, Ptgis, Col26a1, Prrx2, Twist1, Foxd1, Depdc7, Nkx3-1 Traf3ip2, Foxd2, Plat, Nr2f2, Car14, Cdh11, Mest, Cd36, Nrp2, Vcan Negative: Slc2a3, Krt8, Slc2a1, Cldn6, Krt18, Phlda2, Car4, Epcam, Meg3, Ezr F11r, Cldn7, Cdx2, Podxl, Msx1, Spint2, Unc5b, Cystm1, Bex1, Dlk1 Cdh1, Cdx1, Msx2, Spint1, Pmp22, Frem2, Hpn, Nrk, Oat, Tmprss2 PC_ 3 Positive: Arg1, Tcf15, Aldh1a2, Fbn2, Nrk, H19, Anxa5, Rhox5, S100g, Spink1 Trap1a, Tdh, Tmsb10, Amn, Habp2, Meox1, Apom, Apoa1, Morc4, Rbp4 Nrp2, Ppp1r1a, Myl6, Apoa4, Lgals2, Ttr, Cald1, Anxa2, Tmprss2, Apob Negative: Pax6, Sox2, Sfrp1, Hes3, 1700011H14Rik, Slc7a3, Ldha, Sox1, Gabrg1, Fgfbp3 Hes5, Crabp2, Irx3, Sox3, Igfbp2, Slc2a1, Stmn2, Hspe1, Upp1, Fabp5 Aldoa, Arhgef28, Stmn3, Lyar, Pou5f1, Gypa, Pim2, Fam181a, Cpn1, Akr1c19 PC_ 4 Positive: Gypa, Cited4, Klf1, Hbb-bh1, Hba-x, Gata1, Hba-a1, Smim1, Snca, Slc38a5 Blvrb, Trim10, Mrap, Alas2, Acp5, Spns2, Hba-a2, Kcnn4, Chchd10, Sptb Car2, Ermap, Nfe2, Hbb-y, Tspan32, Epor, Tspan33, Tal1, Abcb10, Hemgn Negative: Tuba1a, Igfbp2, Pmp22, Wbp5, Actg1, Cdx2, Cthrc1, Pdgfa, T, Krt18 Krt8, Serpinh1, Vtn, Wfdc2, Wnt6, Ccnd2, Crabp2, Etv4, Map1b, Atp1a2 Ngfrap1, Pla2g7, Cenpf, Stmn2, Bmp4, Sox2, Sox11, Sfrp1, Cdkn1c, Tpm4 PC_ 5 Positive: 1700011H14Rik, Crabp2, Pax6, Hnf1b, Hes3, 1700019D03Rik, Sox2, Fgb, Gabrg1, Sox1 Palm3, Slc7a3, Folr1, Tspan13, Fgfr2, Cpn1, Ptn, Arhgef28, Sox3, Pcbd1 Rfx4, Fgfbp3, Hes5, Fam181a, Stmn3, Akr1c19, Apoa1, 2810459M11Rik, S100g, Upp1 Negative: Tpm1, Serpinh1, Igf2, Acta2, Ahnak, H19, Rgs5, Npm3, Phlda2, Hand1 Krt8, Dok4, Tmsb10, Pmp22, Bmp4, Fam212a, Bambi, Plac8, Krt18, Dlk1 Spin2c, Fn1, Slc25a4, Ppic, Cryab, Gata6, Foxf1, Unc5c, Bmp2, Cgnl1 Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 995 Number of edges: 32157 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.8738 Number of communities: 4 Elapsed time: 0 seconds
07:29:35 UMAP embedding parameters a = 0.9922 b = 1.112 07:29:35 Read 995 rows and found 20 numeric columns 07:29:35 Using Annoy for neighbor search, n_neighbors = 30 07:29:35 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 07:29:35 Writing NN index file to temp file /tmp/Rtmpb4TuY7/file221a4355438e7 07:29:35 Searching Annoy index using 1 thread, search_k = 3000 07:29:35 Annoy recall = 100% 07:29:36 Commencing smooth kNN distance calibration using 1 thread 07:29:36 Initializing from normalized Laplacian + noise 07:29:36 Commencing optimization for 500 epochs, with 36534 positive edges 07:29:40 Optimization finished
DimPlot(meoxg, reduction = 'umap', label = T)
FeaturePlot(meoxg, features = c("Tbx18", "Uncx", "Pax1", "Pax3"))
uncx.g <- subset(x = meoxg, subset = Uncx > 1)
tbx18.g <- subset(x = meoxg, subset = Tbx18 > 0.5)
pax1.g <- subset(x = meoxg, subset = Pax1 > 0.5)
pax3.g <- subset(x = meoxg, subset = Pax3 > 1)
meoxg@meta.data$Anterior <-
ifelse(
rownames(meoxg@meta.data) %in% colnames(tbx18.g), "Anterior", "")
meoxg@meta.data$Posterior <-
ifelse(
rownames(meoxg@meta.data) %in% colnames(uncx.g), "Posterior", "")
meoxg@meta.data$Dorsal <-
ifelse(
rownames(meoxg@meta.data) %in% colnames(pax3.g), "Dorsal", "")
meoxg@meta.data$Ventral <-
ifelse(
rownames(meoxg@meta.data) %in% colnames(pax1.g), "Ventral", "")
meoxg$APIdentity <- paste(meoxg$Posterior,meoxg$Anterior)
meoxg$DVIdentity <- paste(meoxg$Dorsal,meoxg$Ventral)
pl1 <- DimPlot(object = meoxg, group.by = 'APIdentity', pt.size = 1, cols = c("grey", "blue", "green", "red"))
pl2 <- DimPlot(object = meoxg, group.by = 'DVIdentity', pt.size = 1, cols = c("grey", "blue", "green", "red"))
pl1
pl2
meow.g <- SetIdent(meoxg, value = meoxg@meta.data$APIdentity)
meoy.g <- SetIdent(meoxg, value = meoxg@meta.data$DVIdentity)
Amarkers <- FindMarkers(meow.g, ident.1 = " Anterior", only.pos = TRUE)#try using DESeq2, MAST
Pmarkers <- FindMarkers(meow.g, ident.1 = "Posterior ", only.pos = TRUE)
Vmarkers <- FindMarkers(meoy.g, ident.1 = " Ventral", only.pos = TRUE)
Dmarkers <- FindMarkers(meoy.g, ident.1 = "Dorsal ", only.pos = TRUE)
Amarkers
Pmarkers
Vmarkers
Dmarkers
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Tbx18 | 6.021715e-128 | 1.0695467 | 1.000 | 0.190 | 1.216989e-123 |
| Meox1 | 7.888220e-14 | 0.4170477 | 1.000 | 1.000 | 1.594209e-09 |
| Dact2 | 1.217206e-11 | 0.2906366 | 0.622 | 0.364 | 2.459974e-07 |
| Nrp2 | 1.700868e-11 | 0.3627143 | 0.795 | 0.534 | 3.437454e-07 |
| Anxa5 | 2.314815e-11 | 0.5897439 | 0.878 | 0.721 | 4.678241e-07 |
| Nkx3-1 | 3.136858e-11 | 0.3229095 | 0.391 | 0.166 | 6.339589e-07 |
| Glrx | 4.401638e-11 | 0.3984906 | 0.679 | 0.460 | 8.895710e-07 |
| Arg1 | 6.272000e-11 | 0.4488456 | 1.000 | 0.945 | 1.267571e-06 |
| Fbn2 | 3.693610e-10 | 0.3843183 | 0.923 | 0.784 | 7.464787e-06 |
| Ptn | 2.634282e-09 | 0.3820541 | 1.000 | 0.955 | 5.323885e-05 |
| Tcf15 | 3.466310e-09 | 0.4128832 | 0.981 | 0.900 | 7.005412e-05 |
| Six1 | 5.711010e-09 | 0.2753667 | 0.712 | 0.501 | 1.154195e-04 |
| Marcks | 1.267006e-08 | 0.3443600 | 1.000 | 0.982 | 2.560619e-04 |
| Fst | 4.202222e-08 | 0.4459000 | 0.904 | 0.814 | 8.492690e-04 |
| Vcan | 1.266957e-07 | 0.2780644 | 0.929 | 0.774 | 2.560520e-03 |
| Rasl11b | 2.251011e-07 | 0.2524023 | 0.622 | 0.429 | 4.549294e-03 |
| Mest | 2.272296e-07 | 0.3979849 | 0.962 | 0.908 | 4.592311e-03 |
| Foxc2 | 5.108590e-07 | 0.3097722 | 0.917 | 0.825 | 1.032446e-02 |
| Net1 | 1.233799e-06 | 0.2786077 | 0.731 | 0.608 | 2.493508e-02 |
| Phlda1 | 3.094147e-06 | 0.3271917 | 0.718 | 0.548 | 6.253271e-02 |
| Sox4 | 1.411920e-05 | 0.2760613 | 0.962 | 0.936 | 2.853491e-01 |
| Ppp1r1a | 2.583359e-05 | 0.2591381 | 0.904 | 0.826 | 5.220969e-01 |
| Ptch1 | 7.170377e-05 | 0.3110982 | 0.827 | 0.696 | 1.000000e+00 |
| Aldh1a2 | 1.147313e-04 | 0.2800344 | 0.923 | 0.794 | 1.000000e+00 |
| Cnn2 | 3.444124e-04 | 0.2850443 | 0.814 | 0.726 | 1.000000e+00 |
| Cer1 | 7.541663e-04 | 0.2984761 | 0.385 | 0.259 | 1.000000e+00 |
| Cdkn1c | 7.249907e-03 | 0.3526961 | 0.750 | 0.660 | 1.000000e+00 |
| S100g | 7.863172e-01 | 0.3229580 | 0.154 | 0.147 | 1.000000e+00 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Uncx | 6.358545e-55 | 1.8877805 | 1.000 | 0.119 | 1.285062e-50 |
| Tcf15 | 8.248855e-11 | 1.0279588 | 1.000 | 0.910 | 1.667094e-06 |
| Meox2 | 1.951859e-09 | 0.3552190 | 0.656 | 0.206 | 3.944707e-05 |
| Foxd1 | 5.948711e-09 | 0.4570509 | 0.812 | 0.364 | 1.202234e-04 |
| Aldh1a2 | 2.042690e-08 | 0.8641964 | 1.000 | 0.808 | 4.128277e-04 |
| Gm8730 | 9.851860e-06 | 0.4872645 | 1.000 | 1.000 | 1.991061e-01 |
| Hoxb4 | 1.338361e-05 | 0.3417613 | 0.719 | 0.347 | 2.704828e-01 |
| Dmrt2 | 6.366021e-05 | 0.4277241 | 0.500 | 0.222 | 1.000000e+00 |
| Meox1 | 6.463672e-05 | 0.4943687 | 1.000 | 1.000 | 1.000000e+00 |
| Kif26b | 6.669663e-05 | 0.2563126 | 0.594 | 0.278 | 1.000000e+00 |
| Fbn2 | 1.102806e-04 | 0.5857611 | 0.875 | 0.804 | 1.000000e+00 |
| Tpm1 | 2.143458e-04 | 0.3868802 | 1.000 | 0.955 | 1.000000e+00 |
| Net1 | 2.809275e-04 | 0.3940345 | 0.844 | 0.620 | 1.000000e+00 |
| Aldoc | 2.816875e-04 | 0.3589949 | 0.812 | 0.504 | 1.000000e+00 |
| Plppr1 | 2.903811e-04 | 0.3642717 | 0.500 | 0.237 | 1.000000e+00 |
| Surf6 | 4.005460e-04 | 0.2923851 | 0.656 | 0.407 | 1.000000e+00 |
| Polr2m | 4.528797e-04 | 0.2633788 | 1.000 | 0.997 | 1.000000e+00 |
| Arl4a | 4.532590e-04 | 0.5216700 | 0.844 | 0.647 | 1.000000e+00 |
| Sox11 | 4.914634e-04 | 0.3642538 | 0.969 | 0.966 | 1.000000e+00 |
| Gpc3 | 5.394240e-04 | 0.4095900 | 1.000 | 0.962 | 1.000000e+00 |
| Arid3a | 8.631114e-04 | 0.3244606 | 0.938 | 0.830 | 1.000000e+00 |
| Gstm5 | 8.660351e-04 | 0.3591263 | 1.000 | 0.946 | 1.000000e+00 |
| H1f0 | 9.112438e-04 | 0.3826968 | 0.969 | 0.886 | 1.000000e+00 |
| Lpar6 | 9.703875e-04 | 0.2574941 | 0.719 | 0.512 | 1.000000e+00 |
| Fst | 9.925243e-04 | 0.5532428 | 0.969 | 0.823 | 1.000000e+00 |
| Capns1 | 1.220676e-03 | 0.2765260 | 0.969 | 0.821 | 1.000000e+00 |
| Bex4 | 1.226498e-03 | 0.3361166 | 1.000 | 0.943 | 1.000000e+00 |
| Gas8 | 1.418824e-03 | 0.2549722 | 0.719 | 0.480 | 1.000000e+00 |
| Zcchc3 | 1.484140e-03 | 0.3301711 | 0.938 | 0.867 | 1.000000e+00 |
| Aif1l | 1.529410e-03 | 0.2829655 | 0.750 | 0.521 | 1.000000e+00 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Bex1 | 0.004156904 | 0.2981141 | 1.000 | 0.968 | 1 |
| Tagln2 | 0.005227956 | 0.3173297 | 0.875 | 0.787 | 1 |
| Pcnp | 0.005573640 | 0.2560678 | 0.969 | 0.982 | 1 |
| Jam3 | 0.005799262 | 0.2764116 | 0.875 | 0.823 | 1 |
| Smco4 | 0.006468126 | 0.3013972 | 0.906 | 0.796 | 1 |
| Mest | 0.006707005 | 0.2548105 | 1.000 | 0.914 | 1 |
| Zc3hc1 | 0.006934158 | 0.2532370 | 0.969 | 0.737 | 1 |
| Cdk11b | 0.006947852 | 0.2658524 | 0.906 | 0.858 | 1 |
| Gdi1 | 0.007403684 | 0.2579033 | 0.594 | 0.455 | 1 |
| Gpatch4 | 0.008764488 | 0.2753704 | 0.906 | 0.882 | 1 |
| Snrpn | 0.012191346 | 0.2837708 | 0.875 | 0.783 | 1 |
| Fam64a | 0.013674457 | 0.3265509 | 1.000 | 0.876 | 1 |
| Arg1 | 0.014701873 | 0.3611870 | 1.000 | 0.952 | 1 |
| Crabp1 | 0.016683979 | 0.2851589 | 1.000 | 0.931 | 1 |
| Sephs2 | 0.016913685 | 0.2872549 | 0.781 | 0.745 | 1 |
| Dact1 | 0.017813803 | 0.2998574 | 0.625 | 0.503 | 1 |
| Hmga1 | 0.018465894 | 0.3319245 | 0.719 | 0.570 | 1 |
| Mycn | 0.020184417 | 0.2739039 | 0.875 | 0.751 | 1 |
| Aplnr | 0.027144342 | 0.2805828 | 0.656 | 0.485 | 1 |
| Umps | 0.029818451 | 0.2710596 | 1.000 | 0.854 | 1 |
| Mtss1 | 0.037308602 | 0.2772895 | 0.469 | 0.368 | 1 |
| Ppp1r1a | 0.042113457 | 0.2974239 | 0.875 | 0.837 | 1 |
| Wfdc21 | 0.043499820 | 0.2971540 | 0.438 | 0.297 | 1 |
| Larp4b | 0.046726565 | 0.2700293 | 0.781 | 0.706 | 1 |
| Tubb2b | 0.049307852 | 0.2730977 | 0.875 | 0.830 | 1 |
| Tm2d3 | 0.077403571 | 0.2652399 | 0.625 | 0.631 | 1 |
| Ncam1 | 0.112375742 | 0.2758041 | 0.719 | 0.722 | 1 |
| Eno1b | 0.134177489 | 0.3825755 | 0.812 | 0.812 | 1 |
| Mis18a | 0.145376260 | 0.3025169 | 0.844 | 0.789 | 1 |
| Brd3 | 0.156030470 | 0.2565902 | 0.969 | 0.946 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Pax1 | 3.620472e-111 | 1.2872568 | 1.0 | 0.010 | 7.316973e-107 |
| Nkx3-1 | 2.414445e-09 | 0.9549723 | 0.9 | 0.194 | 4.879593e-05 |
| Dnal1 | 8.123357e-09 | 0.3323958 | 0.5 | 0.061 | 1.641731e-04 |
| Col1a2 | 2.716801e-07 | 0.8835682 | 0.9 | 0.261 | 5.490654e-03 |
| Zfp185 | 6.081815e-07 | 0.2894549 | 0.5 | 0.077 | 1.229135e-02 |
| Dera | 1.456544e-06 | 0.9915217 | 1.0 | 0.763 | 2.943675e-02 |
| Ets1 | 1.879381e-06 | 0.8559799 | 0.8 | 0.269 | 3.798229e-02 |
| Pon2 | 2.808485e-06 | 0.5580503 | 0.8 | 0.220 | 5.675948e-02 |
| Anxa5 | 5.103188e-06 | 1.3234333 | 1.0 | 0.743 | 1.031354e-01 |
| Nrp1 | 7.489652e-06 | 0.8536008 | 1.0 | 0.413 | 1.513659e-01 |
| Arhgap29 | 1.017744e-05 | 0.5237266 | 0.8 | 0.236 | 2.056861e-01 |
| Ccdc51 | 1.920026e-05 | 0.3207260 | 0.6 | 0.138 | 3.880373e-01 |
| Glrx | 2.921380e-05 | 0.9869760 | 0.9 | 0.490 | 5.904109e-01 |
| Laptm4a | 3.258911e-05 | 0.6044726 | 1.0 | 0.987 | 6.586260e-01 |
| Zfp692 | 3.465632e-05 | 0.3156457 | 0.6 | 0.146 | 7.004042e-01 |
| Col1a1 | 4.341392e-05 | 0.6138273 | 0.6 | 0.161 | 8.773953e-01 |
| Islr | 5.209299e-05 | 0.2725175 | 0.5 | 0.101 | 1.000000e+00 |
| Slc12a4 | 5.735342e-05 | 0.2677635 | 0.4 | 0.074 | 1.000000e+00 |
| Gm8730 | 5.856054e-05 | 0.7500008 | 1.0 | 1.000 | 1.000000e+00 |
| Cuedc1 | 6.470076e-05 | 0.6980265 | 0.9 | 0.401 | 1.000000e+00 |
| Ikzf2 | 7.603726e-05 | 0.3069867 | 0.4 | 0.072 | 1.000000e+00 |
| Nim1k | 8.731584e-05 | 0.2585878 | 0.3 | 0.045 | 1.000000e+00 |
| P3h2 | 8.907766e-05 | 0.5644849 | 0.9 | 0.383 | 1.000000e+00 |
| Pcolce | 9.450015e-05 | 0.6105169 | 0.7 | 0.220 | 1.000000e+00 |
| Snhg18 | 1.249305e-04 | 0.3160408 | 0.6 | 0.166 | 1.000000e+00 |
| Akap7 | 2.023948e-04 | 0.5052658 | 0.7 | 0.224 | 1.000000e+00 |
| Wipi1 | 2.295975e-04 | 0.3995297 | 0.5 | 0.135 | 1.000000e+00 |
| Twist1 | 2.356764e-04 | 0.7677172 | 1.0 | 0.627 | 1.000000e+00 |
| Runx1t1 | 2.514717e-04 | 0.3238584 | 0.5 | 0.124 | 1.000000e+00 |
| Oxr1 | 3.141365e-04 | 0.4543723 | 0.6 | 0.197 | 1.000000e+00 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Arl5b | 0.3866115 | 0.2977403 | 0.6 | 0.510 | 1 |
| Ap2b1 | 0.3886646 | 0.2804733 | 0.6 | 0.543 | 1 |
| Lias | 0.3889919 | 0.2741879 | 0.6 | 0.545 | 1 |
| Ccnd2 | 0.3918875 | 0.3321131 | 0.7 | 0.882 | 1 |
| Chd1l | 0.3935951 | 0.3245352 | 0.3 | 0.248 | 1 |
| Ppif | 0.3962468 | 0.2577175 | 0.7 | 0.564 | 1 |
| Nip7 | 0.4033825 | 0.2539469 | 1.0 | 0.955 | 1 |
| Smek2 | 0.4232338 | 0.2550137 | 0.6 | 0.659 | 1 |
| Wnt2 | 0.4327857 | 0.2547606 | 0.1 | 0.050 | 1 |
| Hand1 | 0.4346981 | 0.2522942 | 0.2 | 0.127 | 1 |
| Lpp | 0.4365694 | 0.2579797 | 0.5 | 0.404 | 1 |
| Ppil4 | 0.5042943 | 0.2888748 | 0.8 | 0.725 | 1 |
| Nfyb | 0.5072295 | 0.2835098 | 0.6 | 0.762 | 1 |
| Evc2 | 0.5241719 | 0.2611477 | 0.2 | 0.152 | 1 |
| Krt18 | 0.5260560 | 0.6061532 | 0.5 | 0.492 | 1 |
| Tpm1 | 0.5507116 | 0.2766725 | 0.8 | 0.958 | 1 |
| Snai1 | 0.5816563 | 0.5388830 | 0.5 | 0.503 | 1 |
| Orc5 | 0.5844323 | 0.2813367 | 0.4 | 0.386 | 1 |
| Srek1 | 0.5916637 | 0.2600805 | 0.9 | 0.870 | 1 |
| Igfbp2 | 0.5982791 | 0.6350994 | 0.4 | 0.517 | 1 |
| S100a10 | 0.6081343 | 0.4557239 | 0.5 | 0.505 | 1 |
| Cetn2 | 0.6134628 | 0.2511018 | 0.7 | 0.736 | 1 |
| Has2 | 0.6854632 | 0.2799458 | 0.2 | 0.154 | 1 |
| Tagln | 0.7120692 | 0.3995947 | 0.3 | 0.276 | 1 |
| Srsf4 | 0.7243809 | 0.2875646 | 0.7 | 0.828 | 1 |
| Nr1h2 | 0.8133402 | 0.3355856 | 0.3 | 0.429 | 1 |
| Pdlim4 | 0.8973299 | 0.2819879 | 0.6 | 0.730 | 1 |
| Pdlim1 | 0.8982292 | 0.3661785 | 0.2 | 0.211 | 1 |
| Nusap1 | 0.9466953 | 0.2750895 | 0.4 | 0.603 | 1 |
| Car2 | 0.9496967 | 0.3235625 | 0.2 | 0.222 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Pax3 | 2.816557e-53 | 1.3451629 | 1.000 | 0.500 | 5.692262e-49 |
| Meox2 | 1.909942e-10 | 0.3835518 | 0.481 | 0.198 | 3.859993e-06 |
| Tcf15 | 2.733765e-10 | 0.6199348 | 0.974 | 0.907 | 5.524939e-06 |
| Crabp1 | 3.303169e-08 | 0.7427751 | 0.948 | 0.932 | 6.675705e-04 |
| Ptn | 2.355479e-07 | 0.3683115 | 1.000 | 0.959 | 4.760422e-03 |
| Aldh1a2 | 4.064803e-07 | 0.5759077 | 0.922 | 0.805 | 8.214967e-03 |
| Meox1 | 6.857042e-07 | 0.4321275 | 1.000 | 1.000 | 1.385808e-02 |
| Ppp1r1a | 9.577961e-06 | 0.4842541 | 0.909 | 0.832 | 1.935706e-01 |
| Cer1 | 1.193053e-05 | 0.5511530 | 0.468 | 0.263 | 2.411161e-01 |
| Dmrt2 | 1.460673e-05 | 0.3333227 | 0.416 | 0.216 | 2.952020e-01 |
| Meis1 | 6.169683e-05 | 0.2637908 | 0.857 | 0.704 | 1.000000e+00 |
| Dact2 | 8.681639e-05 | 0.3351217 | 0.558 | 0.391 | 1.000000e+00 |
| Fcf1 | 1.810078e-04 | 0.2539532 | 0.935 | 0.890 | 1.000000e+00 |
| Pdgfra | 4.419254e-04 | 0.2544396 | 0.714 | 0.584 | 1.000000e+00 |
| Fst | 4.751613e-04 | 0.3989863 | 0.961 | 0.817 | 1.000000e+00 |
| Arg1 | 5.000688e-04 | 0.3664309 | 0.961 | 0.953 | 1.000000e+00 |
| Nrp2 | 5.041400e-04 | 0.2551893 | 0.714 | 0.563 | 1.000000e+00 |
| Gstm5 | 7.334770e-04 | 0.2636843 | 0.948 | 0.948 | 1.000000e+00 |
| Myc | 9.876867e-04 | 0.2517913 | 0.494 | 0.365 | 1.000000e+00 |
| Arl4a | 1.385768e-03 | 0.2746294 | 0.740 | 0.646 | 1.000000e+00 |
emb8@meta.data$Anterior <-
ifelse(
rownames(emb8@meta.data) %in% colnames(tbx18.g), "Anterior", "")
emb8@meta.data$Posterior <-
ifelse(
rownames(emb8@meta.data) %in% colnames(uncx.g), "Posterior", "")
emb8@meta.data$Dorsal <-
ifelse(
rownames(emb8@meta.data) %in% colnames(pax3.g), "Dorsal", "")
emb8@meta.data$Ventral <-
ifelse(
rownames(emb8@meta.data) %in% colnames(pax1.g), "Ventral", "")
emb8$APIdentity <- paste(emb8$Posterior,emb8$Anterior)
emb8$DVIdentity <- paste(emb8$Dorsal,emb8$Ventral)
pl1 <- DimPlot(object = emb8, group.by = 'APIdentity', pt.size = 1, cols = c("grey", "blue", "red", "green"))
pl2 <- DimPlot(object = emb8, group.by = 'DVIdentity', pt.size = 1, cols = c("grey", "blue", "red", "green"))
pl1
pl2
meow <- SetIdent(emb8, value = emb8@meta.data$APIdentity)
meoy <- SetIdent(emb8, value = emb8@meta.data$DVIdentity)
Pmarkers <- FindMarkers(meow, ident.1 = "Posterior ", only.pos = TRUE) #MAST can be tried by using test.use='MAST'
Amarkers <- FindMarkers(meow, ident.1 = " Anterior", only.pos = TRUE)
Vmarkers <- FindMarkers(meoy, ident.1 = " Ventral", only.pos = TRUE)
Dmarkers <- FindMarkers(meoy, ident.1 = "Dorsal ", only.pos = TRUE)
Somite_all_A <- Amarkers[,0]
Somite_all_P <- Pmarkers[,0]
Somite_all_D <- Dmarkers[,0]
Somite_all_V <- Vmarkers[,0]
Somite_all_A <- head(Somite_all_A , n=20)
Somite_all_P <- head(Somite_all_P , n=20)
Somite_all_D <- head(Somite_all_D , n=20)
Somite_all_V <- head(Somite_all_V , n=20)
Somite_all_A
Somite_all_P
Somite_all_D
Somite_all_V
| Tbx18 |
|---|
| Meox2 |
| Meox1 |
| Tcf15 |
| Dmrt2 |
| Halr1 |
| Wfdc21 |
| Foxd2 |
| Foxc2 |
| Nkx3-1 |
| Arg1 |
| Foxd1 |
| Cd36 |
| Aldh1a2 |
| Cer1 |
| Foxc1 |
| Six1 |
| Wnt16 |
| Itih5 |
| Ntn1 |
| Uncx |
|---|
| Meox2 |
| Dmrt2 |
| Foxd1 |
| 2610016A17Rik |
| Meox1 |
| Tcf15 |
| Wfdc21 |
| Halr1 |
| Nkx3-1 |
| Foxc2 |
| Aldh1a2 |
| Cd36 |
| Arg1 |
| Foxd2 |
| Aldoc |
| Stra6 |
| Plppr1 |
| Fst |
| Foxc1 |
| Meox2 |
|---|
| Dmrt2 |
| Meox1 |
| Tcf15 |
| Pax3 |
| Cer1 |
| Wfdc21 |
| Halr1 |
| Foxc2 |
| Arg1 |
| Aldh1a2 |
| Ptgds |
| Foxc1 |
| Fst |
| Cd36 |
| Uncx |
| Foxd1 |
| Tbx18 |
| Ppp1r1a |
| Aldoc |
| Pax1 |
|---|
| Nkx3-1 |
| Islr |
| Foxd2 |
| Cd36 |
| Foxd1 |
| Saa2 |
| Meox1 |
| Halr1 |
| Tcf15 |
| Wnt16 |
| Foxc2 |
| Itih5 |
| Foxp2 |
| Traf3ip2 |
| Aldh1a2 |
| Dusp23 |
| Arg1 |
| Col1a2 |
| Ets1 |
FeaturePlot(Emb8, features = ('Sox2'))
nte <- subset(x = Emb8, subset = Sox1 > 0.5)
nte <- NormalizeData(nte, verbose = TRUE)
nte <- FindVariableFeatures(nte, verbose = TRUE)
nte <- ScaleData(nte, verbose = TRUE)
nte <- RunPCA(nte, features = VariableFeatures(object = nte), verbose = TRUE)
nte <- FindNeighbors(nte, dims = 1:20)
nte <- FindClusters(nte, resolution = 0.2)
nte <- RunUMAP(nte, dims = 1:20)
Centering and scaling data matrix PC_ 1 Positive: Aldoa, Eno1, Tmsb10, Ptn, Tuba1b, Akr1b3, Tmsb4x, Rplp0, Ifitm2, H2afx Map1b, Slc16a3, Prdx2, Sox4, Mest, Rps2, Sms, Stmn1, Fam162a, Igfbp2 Sfrp1, Ddit4, Stmn2, Hspa8, Efna5, Ube2s, Nnat, Pgk1, Cdca8, Fgfr1 Negative: Cubn, Lgals2, Ttr, Apom, Cdhr2, Amn, Ctsh, Spink1, Rbp4, Apoa4 2210011C24Rik, S100g, Apob, Slc2a2, Slc39a5, Clic6, Apoa1, Car7, Tdh, Rhox5 Morc4, Creb3l3, Dab2, Cldn7, Aqp3, Slc13a4, Pdzk1, Npl, Cited1, Rbm47 PC_ 2 Positive: Mest, Pcsk1n, Gm2694, Otx2, Ube2k, Hesx1, Fam162a, Larp7, Dnmt3b, Lhx5 Slc2a3, Pou3f1, Six3, Irs4, Tmsb4x, Cited2, Tubb3, Foxd4, Shisa2, Sparc Pak3, Rax, Fezf1, Wbp5, Ngfrap1, Celf2, Lhx2, Meg3, Pax2, Ddit4 Negative: Mdk, 1700011H14Rik, Hotairm1, Hoxa1, Zfp703, Crabp2, Fgfbp3, Ccnd2, Sfrp5, Rarb Metrn, Hoxd4, Tubb2b, Upp1, Tshz1, Cdx1, Pcbd1, Skap2, Cpn1, Dact2 Ifitm1, Hnf1b, Pax6, Rbp1, Meis2, Spsb4, Ncam1, Gfra3, Pcsk9, Robo2 PC_ 3 Positive: Ptn, Ngfrap1, Fst, Irx3, Irx5, Ezr, Stmn2, Efnb1, Akr1b3, Map1b Rbp1, Vgll3, Tceal8, Fgf15, Crabp1, Meis2, Fabp7, Cited2, Mn1, Sox21 Id2, Foxb1, Gbx2, Mast4, Tmsb4x, Irx2, Fez1, Spc25, Sox13, Igdcc3 Negative: Hesx1, Six3, Irs4, Foxd4, Rax, Shisa2, Fezf1, Ermn, Ina, Eras Otx2, Lhx2, Fzd5, Hba-x, Gypa, Blvrb, Cited4, Klf1, Ube2k, Hba-a1 Hbb-bh1, Sms, Lhx5, Trim10, Snca, Gpc4, Calm1, Gata1, Lmo1, Alas2 PC_ 4 Positive: Igfbp2, Hesx1, Six3, Rax, Irs4, Foxd4, Shisa2, Fezf1, Ermn, Lhx2 Sms, Eras, Pls3, Ina, Emb, Otx2, Ifitm2, Slc2a1, Fzd5, Lhx5 Tubb3, Ube2k, Hdac6, Id3, Wfdc2, Pim2, Spint2, Mdk, Iqgap1, Pcsk1n Negative: Gypa, Cited4, Klf1, Hba-a1, Hbb-bh1, Hba-x, Hba-a2, Blvrb, Smim1, Trim10 Gata1, Alas2, Gfi1b, Lyl1, Acp5, Hemgn, Snca, Nmnat3, Mrap, Slc25a37 Cpox, Car2, Nfe2, Ermap, Ube2l6, Zfpm1, Hbb-bt, Lmo2, Sptb, Tal1 PC_ 5 Positive: Crabp2, Pax6, Ptn, Sfrp1, Slc2a1, Tdh, Tspan13, Hes5, Mt1, Gcsh Pls3, Hebp1, Lgals2, Apoa1, Cdhr2, Rab11a, Rbp4, Uchl1, Hbb-bh1, Ttr Abcc2, Car7, Apom, Aldob, Sfrp5, Sfrp2, Apoa4, Igfbp2, Sigirr, Fxyd2 Negative: Hoxa7, Rspo3, Hoxa9, Slc38a4, Phlda2, Foxf1, Bambi, Eng, Kdr, Igf2 Plac1, Tgfb1, Hoxc6, Hoxd9, Hand1, Hoxa10, Fn1, Amot, Cdx2, Krt8 Etv2, Hoxb6, Bmp4, Hoxa11, Vgll4, Asb4, Twist1, Tmem119, Hand2, Spin2c Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 860 Number of edges: 28643 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.8868 Number of communities: 4 Elapsed time: 0 seconds
08:45:47 UMAP embedding parameters a = 0.9922 b = 1.112 08:45:47 Read 860 rows and found 20 numeric columns 08:45:47 Using Annoy for neighbor search, n_neighbors = 30 08:45:47 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 08:45:47 Writing NN index file to temp file /tmp/Rtmp20JbmP/file182b56eed9ee9 08:45:47 Searching Annoy index using 1 thread, search_k = 3000 08:45:47 Annoy recall = 100% 08:45:47 Commencing smooth kNN distance calibration using 1 thread 08:45:48 Initializing from normalized Laplacian + noise 08:45:48 Commencing optimization for 500 epochs, with 31802 positive edges 08:45:51 Optimization finished
DimPlot(nte, reduction = 'umap', label = T)
nocd <- subset(x = nte, subset = Cdx2 < 0.5)
cdx2 <- subset(x = nte, subset = Cdx2 > 0.5)
nte@meta.data$Anterior <-
ifelse(
rownames(nte@meta.data) %in% colnames(nocd), "Anterior", "")
nte@meta.data$Posterior <-
ifelse(
rownames(nte@meta.data) %in% colnames(cdx2), "Posterior", "")
nte$APIdentity <- paste(nte$Posterior,nte$Anterior)
nte@meta.data
| orig.ident | nCount_RNA | nFeature_RNA | percent.mt | RNA_snn_res.0.5 | seurat_clusters | RNA_snn_res.0.2 | Anterior | Posterior | APIdentity | |
|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <dbl> | <int> | <dbl> | <fct> | <fct> | <fct> | <chr> | <chr> | <chr> | |
| AAACCTGGTATAAACG-1 | Emb8 | 14868 | 3303 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AAACCTGGTCAATACC-1 | Emb8 | 23270 | 4427 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AAACCTGTCATCATTC-1 | Emb8 | 17820 | 4124 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| AAAGATGCACCTATCC-1 | Emb8 | 13161 | 3394 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AAAGCAAGTAGAAGGA-1 | Emb8 | 17271 | 3880 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AAAGTAGTCCAAATGC-1 | Emb8 | 17504 | 4068 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| AACACGTCACTGTGTA-1 | Emb8 | 11889 | 3405 | 0 | 5 | 1 | 1 | Anterior | Anterior | |
| AACCATGGTGACGGTA-1 | Emb8 | 14680 | 3479 | 0 | 17 | 2 | 2 | Anterior | Anterior | |
| AACCATGGTTGTGGAG-1 | Emb8 | 12370 | 3277 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AACCATGTCGAGAACG-1 | Emb8 | 20293 | 4352 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| AACTCAGTCACCAGGC-1 | Emb8 | 48056 | 6520 | 0 | 3 | 0 | 0 | Anterior | Anterior | |
| AACTGGTCATCTGGTA-1 | Emb8 | 21470 | 4305 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| AACTTTCCAGATAATG-1 | Emb8 | 24551 | 4747 | 0 | 6 | 0 | 0 | Anterior | Anterior | |
| AAGACCTCAAGAAGAG-1 | Emb8 | 11248 | 3119 | 0 | 6 | 1 | 1 | Posterior | Posterior | |
| AAGACCTGTTATCGGT-1 | Emb8 | 27536 | 4977 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| AAGCCGCTCGCGTTTC-1 | Emb8 | 15381 | 3842 | 0 | 6 | 1 | 1 | Posterior | Posterior | |
| AAGGTTCAGGATGGTC-1 | Emb8 | 26826 | 4810 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| AAGTCTGCAGCTCGAC-1 | Emb8 | 16991 | 3824 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| AAGTCTGGTGGACGAT-1 | Emb8 | 10718 | 3061 | 0 | 17 | 2 | 2 | Anterior | Anterior | |
| AATCCAGCAGGCGATA-1 | Emb8 | 12808 | 3026 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| AATCCAGGTACTCGCG-1 | Emb8 | 22485 | 4369 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| ACACCCTTCCGCGTTT-1 | Emb8 | 11205 | 3434 | 0 | 6 | 0 | 0 | Anterior | Anterior | |
| ACACCGGCACAGCGTC-1 | Emb8 | 27142 | 4621 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| ACACTGACACATTTCT-1 | Emb8 | 27718 | 5004 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| ACAGCTAAGTCTCAAC-1 | Emb8 | 16864 | 4442 | 0 | 9 | 3 | 3 | Anterior | Anterior | |
| ACAGCTACAATAACGA-1 | Emb8 | 20944 | 4734 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| ACATACGCAGTAACGG-1 | Emb8 | 23134 | 4529 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| ACATCAGCAATCCGAT-1 | Emb8 | 13927 | 3708 | 0 | 2 | 1 | 1 | Anterior | Anterior | |
| ACCAGTAGTGGAAAGA-1 | Emb8 | 29175 | 5050 | 0 | 6 | 1 | 1 | Posterior | Posterior | |
| ACCCACTGTTACCGAT-1 | Emb8 | 36433 | 5456 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| TGTGGTACATATACGC-2 | Emb8 | 14623 | 3689 | 0 | 5 | 1 | 1 | Posterior | Posterior | |
| TGTGGTAGTACCGGCT-2 | Emb8 | 11616 | 3171 | 0 | 6 | 0 | 0 | Anterior | Anterior | |
| TGTGTTTCAGACGTAG-2 | Emb8 | 10988 | 3051 | 0 | 6 | 0 | 0 | Anterior | Anterior | |
| TGTGTTTCAGCTATTG-2 | Emb8 | 16387 | 3991 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TGTGTTTTCGGCTACG-2 | Emb8 | 11235 | 3011 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TGTTCCGCAATAGCGG-2 | Emb8 | 14834 | 3617 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TGTTCCGTCTCCAACC-2 | Emb8 | 17745 | 4174 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTAGGCAAGTCTCAAC-2 | Emb8 | 12168 | 3603 | 0 | 6 | 1 | 1 | Posterior | Posterior | |
| TTAGTTCAGCTACCTA-2 | Emb8 | 12165 | 3421 | 0 | 8 | 1 | 1 | Posterior | Posterior | |
| TTATGCTCATTTGCCC-2 | Emb8 | 40374 | 5994 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTATGCTTCAGCTTAG-2 | Emb8 | 15351 | 3714 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTCCCAGAGTTGAGTA-2 | Emb8 | 38152 | 5370 | 0 | 17 | 2 | 2 | Anterior | Anterior | |
| TTCGAAGCATGCAATC-2 | Emb8 | 16853 | 3943 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTCGGTCAGCGCTCCA-2 | Emb8 | 10412 | 3255 | 0 | 9 | 3 | 3 | Anterior | Anterior | |
| TTCGGTCCATGGATGG-2 | Emb8 | 25221 | 4572 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTCGGTCTCCTATTCA-2 | Emb8 | 19473 | 4187 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTCTTAGAGTGTTAGA-2 | Emb8 | 12664 | 3551 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| TTCTTAGAGTTAGCGG-2 | Emb8 | 20983 | 4386 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTCTTAGGTCCAGTTA-2 | Emb8 | 22412 | 4410 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| TTCTTAGGTCGCGGTT-2 | Emb8 | 14727 | 3687 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTGAACGGTGTGGTTT-2 | Emb8 | 16344 | 3834 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTGACTTGTTTGCATG-2 | Emb8 | 20396 | 4226 | 0 | 7 | 2 | 2 | Anterior | Anterior | |
| TTGTAGGCATGGGACA-2 | Emb8 | 12242 | 3158 | 0 | 6 | 1 | 1 | Posterior | Posterior | |
| TTGTAGGTCATGCATG-2 | Emb8 | 13843 | 3500 | 0 | 6 | 1 | 1 | Anterior | Anterior | |
| TTTACTGAGGCCCTTG-2 | Emb8 | 15251 | 3703 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTTACTGGTATCACCA-2 | Emb8 | 29105 | 4985 | 0 | 5 | 1 | 1 | Posterior | Posterior | |
| TTTATGCCATTTGCTT-2 | Emb8 | 19886 | 4389 | 0 | 4 | 1 | 1 | Anterior | Anterior | |
| TTTCCTCTCAATCTCT-2 | Emb8 | 14542 | 3696 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTTGCGCCAGCGTTCG-2 | Emb8 | 16289 | 3767 | 0 | 2 | 0 | 0 | Anterior | Anterior | |
| TTTGGTTGTCTCTTAT-2 | Emb8 | 14243 | 3527 | 0 | 7 | 2 | 2 | Anterior | Anterior |
pl1 <- DimPlot(object = nte, group.by = 'APIdentity', pt.size = 1, cols = c("red", "blue"))
pl1
ntube.g <- SetIdent(nte, value = nte@meta.data$APIdentity)
Pmarkers <- FindMarkers(ntube.g, ident.2 = " Anterior", ident.1 = "Posterior ", only.pos = TRUE)
Amarkers <- FindMarkers(ntube.g, ident.1 = " Anterior", ident.2 = "Posterior ", only.pos = TRUE)
Pmarkers
Amarkers
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Cdx2 | 7.870845e-133 | 1.2734410 | 1.000 | 0.061 | 1.590698e-128 |
| Hoxaas3 | 6.883421e-40 | 1.4506035 | 0.602 | 0.112 | 1.391139e-35 |
| Hoxb8 | 8.264571e-36 | 0.6151072 | 0.430 | 0.048 | 1.670270e-31 |
| Cdx4 | 2.860034e-34 | 0.5969441 | 0.387 | 0.039 | 5.780129e-30 |
| Hoxa7 | 1.291461e-31 | 0.2867499 | 0.258 | 0.012 | 2.610042e-27 |
| Cdx1 | 2.617210e-31 | 1.4180819 | 0.677 | 0.193 | 5.289382e-27 |
| Hoxb9 | 1.036597e-28 | 0.5935068 | 0.290 | 0.023 | 2.094962e-24 |
| Hoxb4 | 2.064278e-26 | 0.4011345 | 0.441 | 0.074 | 4.171906e-22 |
| Hoxc8 | 1.152972e-19 | 0.3462347 | 0.204 | 0.017 | 2.330155e-15 |
| Etv4 | 1.244656e-19 | 0.3598788 | 0.355 | 0.069 | 2.515449e-15 |
| Nkx1-2 | 1.364151e-18 | 0.5498408 | 0.462 | 0.130 | 2.756948e-14 |
| Hoxb5os | 7.769659e-18 | 0.4090508 | 0.312 | 0.056 | 1.570248e-13 |
| Hoxb1 | 8.087882e-16 | 0.5208351 | 0.591 | 0.233 | 1.634561e-11 |
| Rspo3 | 1.673022e-15 | 0.3897145 | 0.247 | 0.040 | 3.381178e-11 |
| Prickle1 | 2.674471e-15 | 0.2829943 | 0.409 | 0.115 | 5.405106e-11 |
| Msx1 | 2.358655e-11 | 0.6223586 | 0.441 | 0.172 | 4.766841e-07 |
| Hoxa1 | 2.607511e-11 | 0.4877749 | 0.677 | 0.331 | 5.269781e-07 |
| Hotairm1 | 7.472529e-10 | 0.4158615 | 0.645 | 0.329 | 1.510198e-05 |
| Calm1 | 2.222620e-09 | 0.4821894 | 1.000 | 1.000 | 4.491916e-05 |
| Fgfbp3 | 7.342336e-09 | 0.5167814 | 0.484 | 0.244 | 1.483886e-04 |
| Oat | 9.560519e-09 | 0.4911103 | 0.763 | 0.606 | 1.932181e-04 |
| Grsf1 | 1.542395e-08 | 0.6953805 | 0.817 | 0.709 | 3.117180e-04 |
| Lef1 | 5.187620e-08 | 0.3476868 | 0.570 | 0.336 | 1.048418e-03 |
| Mdk | 7.759613e-08 | 0.3388947 | 1.000 | 0.999 | 1.568218e-03 |
| Hoxb2 | 8.274437e-08 | 0.3011070 | 0.591 | 0.322 | 1.672264e-03 |
| Lhpp | 1.238277e-07 | 0.3288667 | 0.613 | 0.375 | 2.502559e-03 |
| Csrp2 | 1.818604e-07 | 0.4091998 | 0.914 | 0.832 | 3.675398e-03 |
| Gtf3a | 1.994203e-07 | 0.2964513 | 0.849 | 0.684 | 4.030283e-03 |
| Pim1 | 2.048036e-07 | 0.3512753 | 0.645 | 0.420 | 4.139080e-03 |
| T | 2.238762e-07 | 0.2739610 | 0.118 | 0.021 | 4.524538e-03 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Ncam1 | 0.003631491 | 0.2971453 | 0.548 | 0.454 | 1 |
| Slc38a4 | 0.004595507 | 0.2907529 | 0.301 | 0.193 | 1 |
| Epcam | 0.004797541 | 0.2509149 | 0.366 | 0.256 | 1 |
| S100a10 | 0.006645124 | 0.3922123 | 0.602 | 0.528 | 1 |
| Serpinh1 | 0.006907308 | 0.2914749 | 0.978 | 0.966 | 1 |
| Amot | 0.007129968 | 0.4194317 | 0.323 | 0.240 | 1 |
| Cited1 | 0.007447118 | 0.5560943 | 0.398 | 0.286 | 1 |
| Lgmn | 0.010546857 | 0.3483111 | 0.602 | 0.498 | 1 |
| Cnn2 | 0.011128385 | 0.3135438 | 0.548 | 0.439 | 1 |
| Ldhb | 0.012406462 | 0.3702021 | 0.892 | 0.854 | 1 |
| Phlda2 | 0.019232481 | 1.3746144 | 0.581 | 0.545 | 1 |
| Igfbp4 | 0.021335216 | 0.4082529 | 0.688 | 0.641 | 1 |
| Reep5 | 0.023288293 | 0.2769045 | 0.634 | 0.570 | 1 |
| Dlk1 | 0.087139392 | 0.2784885 | 0.237 | 0.176 | 1 |
| Bambi | 0.087223561 | 0.2580346 | 0.301 | 0.254 | 1 |
| Apom | 0.123621712 | 0.5492943 | 0.269 | 0.214 | 1 |
| Tpm1 | 0.137861747 | 0.2814087 | 0.828 | 0.851 | 1 |
| Apoa1 | 0.206566750 | 0.7606218 | 0.419 | 0.499 | 1 |
| Krt8 | 0.209822548 | 0.7128903 | 0.419 | 0.403 | 1 |
| Apoa2 | 0.234230106 | 0.5322595 | 0.204 | 0.166 | 1 |
| Rbp4 | 0.315130811 | 0.7590549 | 0.366 | 0.426 | 1 |
| Spink1 | 0.316348312 | 0.6220570 | 0.301 | 0.276 | 1 |
| Mt1 | 0.328871787 | 0.3308571 | 0.366 | 0.459 | 1 |
| Ttr | 0.364106484 | 1.0493340 | 0.430 | 0.405 | 1 |
| Hbb-bh1 | 0.370892872 | 0.4637028 | 0.882 | 0.939 | 1 |
| Krt18 | 0.473389346 | 0.5300806 | 0.473 | 0.480 | 1 |
| Cldn6 | 0.484643937 | 0.3012161 | 0.194 | 0.172 | 1 |
| Hba-x | 0.629336624 | 0.3598157 | 0.935 | 0.905 | 1 |
| Hba-a1 | 0.669912488 | 0.3238389 | 0.495 | 0.485 | 1 |
| Apoe | 0.916876501 | 0.4816499 | 0.849 | 0.907 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Pkm | 1.667378e-11 | 0.2781734 | 1.000 | 1.000 | 3.369771e-07 |
| Prmt1 | 4.422309e-10 | 0.2576402 | 1.000 | 1.000 | 8.937485e-06 |
| Pou3f1 | 9.591089e-10 | 0.5572552 | 0.741 | 0.484 | 1.938359e-05 |
| Ptn | 1.207244e-09 | 0.7946547 | 0.982 | 0.892 | 2.439841e-05 |
| Pgam1 | 1.388856e-08 | 0.2551607 | 1.000 | 1.000 | 2.806879e-04 |
| Gnai2 | 1.831169e-08 | 0.3380259 | 0.999 | 0.978 | 3.700792e-04 |
| Ddit4 | 5.470641e-08 | 0.5828144 | 0.747 | 0.484 | 1.105617e-03 |
| Tpi1 | 6.703900e-08 | 0.2572352 | 1.000 | 1.000 | 1.354858e-03 |
| Irx5 | 7.095569e-08 | 0.3709271 | 0.443 | 0.161 | 1.434014e-03 |
| Fgf15 | 7.123641e-08 | 0.5000252 | 0.555 | 0.258 | 1.439688e-03 |
| Sox2 | 1.327683e-07 | 0.4329281 | 0.860 | 0.710 | 2.683248e-03 |
| Lhx5 | 2.010628e-07 | 0.3912518 | 0.300 | 0.043 | 4.063478e-03 |
| Tuba1a | 2.998387e-07 | 0.2983779 | 0.996 | 0.978 | 6.059740e-03 |
| Sox21 | 3.804841e-07 | 0.3530886 | 0.531 | 0.247 | 7.689584e-03 |
| Irx3 | 6.855161e-07 | 0.5300039 | 0.625 | 0.430 | 1.385428e-02 |
| H2afv | 1.329145e-06 | 0.2648859 | 0.999 | 0.978 | 2.686201e-02 |
| Kif21a | 1.963651e-06 | 0.3027553 | 0.578 | 0.312 | 3.968539e-02 |
| Ifitm2 | 2.018168e-06 | 0.3799954 | 0.984 | 0.957 | 4.078718e-02 |
| Otx2 | 3.148401e-06 | 0.4356386 | 0.297 | 0.065 | 6.362918e-02 |
| Socs2 | 3.661219e-06 | 0.2794554 | 0.495 | 0.237 | 7.399323e-02 |
| Jam3 | 4.267504e-06 | 0.3013123 | 0.765 | 0.548 | 8.624625e-02 |
| Map1b | 8.070384e-06 | 0.3803494 | 0.969 | 0.882 | 1.631025e-01 |
| Pcsk1n | 9.522885e-06 | 0.3856277 | 0.557 | 0.323 | 1.924575e-01 |
| Pax2 | 9.889181e-06 | 0.2709614 | 0.291 | 0.086 | 1.998604e-01 |
| Med22 | 1.244743e-05 | 0.2504645 | 0.592 | 0.355 | 2.515625e-01 |
| Bnip3l | 1.248134e-05 | 0.2843731 | 0.999 | 0.989 | 2.522478e-01 |
| Pls3 | 1.287275e-05 | 0.3330647 | 0.833 | 0.667 | 2.601584e-01 |
| Irs4 | 1.405117e-05 | 0.3159686 | 0.258 | 0.054 | 2.839742e-01 |
| Ube2k | 1.619535e-05 | 0.3635416 | 0.991 | 0.946 | 3.273080e-01 |
| Ngfrap1 | 2.572664e-05 | 0.3252619 | 1.000 | 1.000 | 5.199354e-01 |
| Mest | 2.749000e-05 | 0.3338718 | 0.949 | 0.860 | 5.555729e-01 |
| Tmsb4x | 2.803353e-05 | 0.4169109 | 0.999 | 0.989 | 5.665576e-01 |
| Tubb3 | 3.619823e-05 | 0.3354730 | 0.609 | 0.387 | 7.315662e-01 |
| Sparc | 4.073164e-05 | 0.2613413 | 0.907 | 0.763 | 8.231865e-01 |
| Gm2694 | 4.968163e-05 | 0.2774845 | 0.488 | 0.269 | 1.000000e+00 |
| Nnat | 6.799253e-05 | 0.3602319 | 0.904 | 0.796 | 1.000000e+00 |
| Fam181b | 7.149344e-05 | 0.4779249 | 0.326 | 0.140 | 1.000000e+00 |
| Fam162a | 1.153463e-04 | 0.2867387 | 0.996 | 0.978 | 1.000000e+00 |
| Sox4 | 1.887703e-04 | 0.2955165 | 0.932 | 0.839 | 1.000000e+00 |
| Hesx1 | 3.056619e-04 | 0.7730107 | 0.232 | 0.075 | 1.000000e+00 |
| Larp7 | 4.174546e-04 | 0.3689686 | 0.945 | 0.935 | 1.000000e+00 |
| Tceal8 | 4.546018e-04 | 0.2762623 | 0.988 | 0.968 | 1.000000e+00 |
| Dnmt3b | 4.966243e-04 | 0.3211888 | 0.857 | 0.785 | 1.000000e+00 |
| Id3 | 5.934857e-04 | 0.3528301 | 0.982 | 0.957 | 1.000000e+00 |
| Dbn1 | 6.341884e-04 | 0.2518468 | 0.874 | 0.742 | 1.000000e+00 |
| Id2 | 7.730039e-04 | 0.4186421 | 0.932 | 0.882 | 1.000000e+00 |
| Pim2 | 7.785360e-04 | 0.3720322 | 0.542 | 0.409 | 1.000000e+00 |
| En1 | 9.196318e-04 | 0.4264062 | 0.205 | 0.065 | 1.000000e+00 |
| Sfrp1 | 1.460498e-03 | 0.3073564 | 0.961 | 0.817 | 1.000000e+00 |
| Foxd4 | 1.636595e-03 | 0.2651011 | 0.180 | 0.054 | 1.000000e+00 |
| Stmn2 | 4.271062e-03 | 0.3428812 | 0.906 | 0.796 | 1.000000e+00 |
| Rax | 5.489411e-03 | 0.3301382 | 0.145 | 0.043 | 1.000000e+00 |
| Six3 | 8.026804e-03 | 0.5206393 | 0.193 | 0.086 | 1.000000e+00 |
| Hes5 | 1.023345e-02 | 0.3177968 | 0.682 | 0.602 | 1.000000e+00 |
| Ezr | 8.019536e-02 | 0.2601636 | 0.828 | 0.806 | 1.000000e+00 |
| Hbb-y | 2.049072e-01 | 0.3246045 | 0.424 | 0.344 | 1.000000e+00 |
| Xist | 2.441638e-01 | 0.2760031 | 0.492 | 0.462 | 1.000000e+00 |
| Fst | 3.164499e-01 | 0.3077249 | 0.492 | 0.452 | 1.000000e+00 |
| Nkx2-9 | 4.113925e-01 | 0.2572813 | 0.134 | 0.108 | 1.000000e+00 |
| Crabp1 | 4.543087e-01 | 0.4860803 | 0.725 | 0.796 | 1.000000e+00 |
emb8@meta.data$Anterior <-
ifelse(
rownames(emb8@meta.data) %in% colnames(nocd), "Anterior", "")
emb8@meta.data$Posterior <-
ifelse(
rownames(emb8@meta.data) %in% colnames(cdx2), "Posterior", "")
emb8$NTIdentity <- paste(emb8$Posterior,emb8$Anterior)
pl1 <- DimPlot(object = emb8, group.by = 'NTIdentity', pt.size = 1, cols = c("grey", "red", "blue"))
pl1
ntube.g <- SetIdent(emb8, value = emb8@meta.data$APIdentity)
Pmarkers <- FindMarkers(ntube.g, ident.1 = "Posterior ", only.pos = TRUE)
Amarkers <- FindMarkers(ntube.g, ident.1 = " Anterior", only.pos = TRUE)
Pmarkers
Amarkers
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Uncx | 0.000000e+00 | 1.9994574 | 1.000 | 0.013 | 0.000000e+00 |
| Meox2 | 1.397468e-210 | 0.5464611 | 0.656 | 0.013 | 2.824284e-206 |
| Dmrt2 | 3.055476e-106 | 0.6631530 | 0.500 | 0.015 | 6.175116e-102 |
| Foxd1 | 9.232280e-70 | 0.7516719 | 0.812 | 0.063 | 1.865844e-65 |
| 2610016A17Rik | 1.036348e-60 | 0.2667935 | 0.281 | 0.009 | 2.094459e-56 |
| Meox1 | 1.103060e-58 | 2.7583076 | 1.000 | 0.131 | 2.229283e-54 |
| Tcf15 | 1.879480e-55 | 3.4497123 | 1.000 | 0.144 | 3.798429e-51 |
| Wfdc21 | 5.332697e-55 | 0.6485206 | 0.438 | 0.023 | 1.077738e-50 |
| Halr1 | 1.942261e-47 | 0.4140141 | 0.469 | 0.030 | 3.925309e-43 |
| Nkx3-1 | 1.287925e-43 | 0.5054500 | 0.406 | 0.025 | 2.602896e-39 |
| Foxc2 | 1.046463e-41 | 1.2780815 | 0.906 | 0.139 | 2.114902e-37 |
| Aldh1a2 | 3.437156e-37 | 2.8879228 | 1.000 | 0.236 | 6.946493e-33 |
| Cd36 | 3.450337e-35 | 0.3234203 | 0.406 | 0.031 | 6.973130e-31 |
| Arg1 | 2.952695e-33 | 2.3952222 | 1.000 | 0.243 | 5.967397e-29 |
| Foxd2 | 7.385571e-30 | 0.3222509 | 0.406 | 0.035 | 1.492624e-25 |
| Aldoc | 2.208529e-28 | 0.8741418 | 0.812 | 0.158 | 4.463437e-24 |
| Stra6 | 6.710503e-28 | 0.6158239 | 0.844 | 0.161 | 1.356193e-23 |
| Plppr1 | 2.858213e-25 | 0.5375180 | 0.500 | 0.063 | 5.776449e-21 |
| Fst | 8.183685e-23 | 1.9071814 | 0.969 | 0.323 | 1.653923e-18 |
| Foxc1 | 1.054070e-22 | 0.5404909 | 0.750 | 0.149 | 2.130276e-18 |
| Depdc7 | 1.263358e-19 | 0.4538415 | 0.594 | 0.107 | 2.553247e-15 |
| Pax3 | 4.936305e-19 | 0.6624607 | 0.719 | 0.163 | 9.976273e-15 |
| Kif26b | 1.218241e-18 | 0.3994632 | 0.594 | 0.111 | 2.462065e-14 |
| Cadm1 | 1.410996e-18 | 1.2352397 | 1.000 | 0.686 | 2.851623e-14 |
| Car8 | 6.664812e-17 | 0.2700395 | 0.344 | 0.044 | 1.346959e-12 |
| Fbn2 | 1.045076e-16 | 1.3782476 | 0.875 | 0.404 | 2.112098e-12 |
| Ppp1r1a | 3.434345e-16 | 1.3249444 | 0.875 | 0.376 | 6.940810e-12 |
| Pgm5 | 3.908548e-16 | 0.2980446 | 0.344 | 0.045 | 7.899176e-12 |
| Ntn1 | 1.019115e-15 | 0.4864396 | 0.562 | 0.123 | 2.059631e-11 |
| Rtn1 | 1.151890e-15 | 0.5490525 | 0.719 | 0.203 | 2.327970e-11 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Sap30 | 0.006873094 | 0.2682768 | 1.000 | 0.790 | 1 |
| Ranbp3 | 0.007050780 | 0.2842174 | 0.812 | 0.693 | 1 |
| Hsd17b10 | 0.007626307 | 0.3146634 | 0.938 | 0.934 | 1 |
| Hnrnph3 | 0.008347002 | 0.2767326 | 0.969 | 0.920 | 1 |
| Mis18a | 0.008825417 | 0.4529498 | 0.844 | 0.732 | 1 |
| Hmga1 | 0.008883584 | 0.3718867 | 0.719 | 0.570 | 1 |
| Hdgfrp3 | 0.008927049 | 0.2592345 | 0.688 | 0.493 | 1 |
| Parl | 0.008969176 | 0.2536619 | 0.969 | 0.910 | 1 |
| Prpf4 | 0.009007547 | 0.2530831 | 0.750 | 0.625 | 1 |
| Igf2bp2 | 0.009088276 | 0.2556107 | 0.875 | 0.808 | 1 |
| Srsf10 | 0.010026954 | 0.2652638 | 1.000 | 0.971 | 1 |
| Mettl9 | 0.011162018 | 0.2648959 | 1.000 | 0.932 | 1 |
| Mcm6 | 0.011245162 | 0.2554617 | 0.938 | 0.927 | 1 |
| Btf3l4 | 0.013484330 | 0.2766930 | 1.000 | 0.946 | 1 |
| E130012A19Rik | 0.013605651 | 0.2505807 | 0.906 | 0.742 | 1 |
| Trim37 | 0.014127125 | 0.2560223 | 0.625 | 0.438 | 1 |
| Prim1 | 0.014874944 | 0.2787811 | 0.812 | 0.693 | 1 |
| Mtss1 | 0.017919896 | 0.2863756 | 0.469 | 0.341 | 1 |
| Zmym4 | 0.018760149 | 0.2552893 | 0.594 | 0.447 | 1 |
| Ctbp2 | 0.019530182 | 0.2596800 | 0.844 | 0.746 | 1 |
| Eno1b | 0.019779201 | 0.5890729 | 0.812 | 0.774 | 1 |
| Hist1h2ap | 0.029349879 | 0.2521073 | 0.844 | 0.815 | 1 |
| Fbxo5 | 0.032597733 | 0.2592510 | 0.781 | 0.739 | 1 |
| Umps | 0.034947995 | 0.2520678 | 1.000 | 0.846 | 1 |
| Naa40 | 0.041756692 | 0.2681602 | 0.531 | 0.427 | 1 |
| Tyms | 0.047987620 | 0.2611897 | 1.000 | 0.938 | 1 |
| Tm2d3 | 0.049568651 | 0.2839283 | 0.625 | 0.617 | 1 |
| Zfp422 | 0.154152946 | 0.2515472 | 0.625 | 0.639 | 1 |
| Dpysl2 | 0.247761172 | 0.2616108 | 0.531 | 0.497 | 1 |
| Nusap1 | 0.253163402 | 0.3526915 | 0.594 | 0.558 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Tbx18 | 0.000000e+00 | 1.1690860 | 1.000 | 0.027 | 0.000000e+00 |
| Meox2 | 0.000000e+00 | 0.3565805 | 0.391 | 0.011 | 0.000000e+00 |
| Meox1 | 7.183037e-278 | 2.6724839 | 1.000 | 0.125 | 1.451692e-273 |
| Tcf15 | 9.289764e-241 | 2.8553316 | 0.981 | 0.139 | 1.877461e-236 |
| Dmrt2 | 4.832889e-229 | 0.3642579 | 0.340 | 0.014 | 9.767269e-225 |
| Halr1 | 3.295425e-223 | 0.4334534 | 0.468 | 0.027 | 6.660055e-219 |
| Wfdc21 | 3.558897e-212 | 0.4556079 | 0.397 | 0.020 | 7.192532e-208 |
| Foxd2 | 4.381412e-206 | 0.4448116 | 0.487 | 0.032 | 8.854833e-202 |
| Foxc2 | 1.392059e-200 | 1.3493483 | 0.917 | 0.135 | 2.813350e-196 |
| Nkx3-1 | 3.411405e-191 | 0.5408431 | 0.391 | 0.022 | 6.894449e-187 |
| Arg1 | 3.530467e-159 | 2.4560501 | 1.000 | 0.238 | 7.135074e-155 |
| Foxd1 | 2.351741e-150 | 0.5129673 | 0.571 | 0.061 | 4.752869e-146 |
| Cd36 | 7.102077e-141 | 0.3214187 | 0.378 | 0.028 | 1.435330e-136 |
| Aldh1a2 | 5.381174e-131 | 2.3237116 | 0.923 | 0.232 | 1.087535e-126 |
| Cer1 | 8.507969e-126 | 0.8084426 | 0.385 | 0.033 | 1.719461e-121 |
| Foxc1 | 1.312491e-122 | 0.6740579 | 0.788 | 0.145 | 2.652544e-118 |
| Six1 | 8.926208e-116 | 0.6279574 | 0.712 | 0.119 | 1.803987e-111 |
| Wnt16 | 3.190339e-115 | 0.2723506 | 0.301 | 0.022 | 6.447675e-111 |
| Itih5 | 1.654338e-108 | 0.4135459 | 0.462 | 0.055 | 3.343418e-104 |
| Ntn1 | 7.098416e-98 | 0.4894283 | 0.660 | 0.120 | 1.434590e-93 |
| Fst | 5.302843e-87 | 1.7622669 | 0.904 | 0.319 | 1.071705e-82 |
| Stra6 | 2.302360e-85 | 0.6044083 | 0.699 | 0.158 | 4.653069e-81 |
| Traf3ip2 | 4.434137e-84 | 0.5272081 | 0.519 | 0.090 | 8.961391e-80 |
| Pax3 | 1.248027e-81 | 0.5814096 | 0.724 | 0.160 | 2.522262e-77 |
| Ppp1r1a | 6.297907e-77 | 1.2658066 | 0.904 | 0.372 | 1.272807e-72 |
| Fbn2 | 5.371946e-73 | 1.1423702 | 0.923 | 0.401 | 1.085670e-68 |
| Dusp23 | 8.085458e-73 | 0.4388130 | 0.571 | 0.118 | 1.634071e-68 |
| Plat | 1.218937e-71 | 0.4779902 | 0.609 | 0.132 | 2.463472e-67 |
| Foxp2 | 1.312399e-68 | 0.2607499 | 0.327 | 0.041 | 2.652359e-64 |
| Depdc7 | 1.310828e-66 | 0.3691028 | 0.526 | 0.104 | 2.649184e-62 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Prkra | 2.114420e-11 | 0.2936354 | 0.846 | 0.695 | 4.273242e-07 |
| Adgrl2 | 2.329235e-11 | 0.3154396 | 0.679 | 0.446 | 4.707383e-07 |
| Btg1 | 3.658459e-11 | 0.3034738 | 0.840 | 0.634 | 7.393746e-07 |
| Pcdh8 | 4.020523e-11 | 0.3089394 | 0.109 | 0.026 | 8.125476e-07 |
| Malat1 | 5.251136e-11 | 0.3164166 | 1.000 | 0.998 | 1.061255e-06 |
| Dkc1 | 5.404596e-11 | 0.3118558 | 0.994 | 0.939 | 1.092269e-06 |
| Dnajc1 | 1.999224e-10 | 0.2533729 | 0.615 | 0.420 | 4.040432e-06 |
| Ctnnb1 | 2.092673e-10 | 0.2663098 | 0.994 | 0.951 | 4.229293e-06 |
| Hnrnph2 | 2.360674e-10 | 0.2612093 | 0.897 | 0.764 | 4.770922e-06 |
| Smarcc1 | 2.461535e-10 | 0.2836756 | 0.994 | 0.949 | 4.974763e-06 |
| Itm2c | 2.658364e-10 | 0.2740848 | 0.859 | 0.674 | 5.372554e-06 |
| Fign | 3.328037e-10 | 0.2503556 | 0.603 | 0.379 | 6.725962e-06 |
| Ptov1 | 4.198635e-10 | 0.2842163 | 1.000 | 0.985 | 8.485441e-06 |
| Acvr2b | 5.666635e-10 | 0.2549245 | 0.897 | 0.738 | 1.145227e-05 |
| E130012A19Rik | 6.138216e-10 | 0.2911799 | 0.910 | 0.741 | 1.240534e-05 |
| Anp32e | 6.416704e-10 | 0.2724149 | 1.000 | 0.976 | 1.296816e-05 |
| Dcakd | 8.763620e-10 | 0.2681954 | 0.987 | 0.916 | 1.771128e-05 |
| Cdca2 | 1.096079e-09 | 0.3031369 | 0.705 | 0.558 | 2.215175e-05 |
| Hsd17b10 | 1.759130e-09 | 0.2685410 | 0.981 | 0.934 | 3.555201e-05 |
| Notch1 | 2.902236e-09 | 0.2637782 | 0.385 | 0.199 | 5.865418e-05 |
| Arl3 | 3.195412e-09 | 0.2760108 | 0.936 | 0.868 | 6.457928e-05 |
| Mis18a | 4.263104e-09 | 0.3009394 | 0.833 | 0.732 | 8.615732e-05 |
| Cnot2 | 9.937218e-09 | 0.2500225 | 0.808 | 0.667 | 2.008312e-04 |
| Tubb2b | 1.089456e-08 | 0.3203554 | 0.821 | 0.636 | 2.201791e-04 |
| Tbl1x | 1.206481e-08 | 0.2605653 | 0.904 | 0.817 | 2.438297e-04 |
| Eva1b | 1.983827e-08 | 0.2840800 | 0.628 | 0.439 | 4.009315e-04 |
| Dera | 2.105639e-07 | 0.2736740 | 0.795 | 0.702 | 4.255496e-03 |
| Laptm4b | 9.973354e-07 | 0.2819543 | 0.814 | 0.702 | 2.015615e-02 |
| Supt16 | 4.607642e-06 | 0.2558366 | 0.923 | 0.916 | 9.312045e-02 |
| Cyr61 | 5.563841e-06 | 0.3226987 | 0.795 | 0.641 | 1.124452e-01 |
Pmarkers <- FindMarkers(ntube.g, ident.1 = "Posterior ", only.pos = TRUE, test.use = "MAST")
Amarkers <- FindMarkers(ntube.g, ident.1 = " Anterior", only.pos = TRUE, test.use = "MAST")
Pmarkers
Amarkers
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| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Uncx | 5.857140e-119 | 1.9994574 | 1.000 | 0.013 | 1.183728e-114 |
| Tcf15 | 1.276736e-54 | 3.4497123 | 1.000 | 0.144 | 2.580284e-50 |
| Aldh1a2 | 1.136795e-50 | 2.8879228 | 1.000 | 0.236 | 2.297462e-46 |
| Meox1 | 2.741555e-47 | 2.7583076 | 1.000 | 0.131 | 5.540683e-43 |
| Arg1 | 5.500532e-34 | 2.3952222 | 1.000 | 0.243 | 1.111657e-29 |
| Fbn2 | 4.564048e-33 | 1.3782476 | 0.875 | 0.404 | 9.223941e-29 |
| Meox2 | 3.752744e-31 | 0.5464611 | 0.656 | 0.013 | 7.584295e-27 |
| Cadm1 | 4.818331e-29 | 1.2352397 | 1.000 | 0.686 | 9.737847e-25 |
| Foxc2 | 1.385861e-27 | 1.2780815 | 0.906 | 0.139 | 2.800825e-23 |
| Foxd1 | 1.429284e-26 | 0.7516719 | 0.812 | 0.063 | 2.888582e-22 |
| Ppp1r1a | 1.661178e-24 | 1.3249444 | 0.875 | 0.376 | 3.357240e-20 |
| Gstm5 | 1.897813e-23 | 1.1255768 | 1.000 | 0.798 | 3.835479e-19 |
| Fst | 6.070588e-23 | 1.9071814 | 0.969 | 0.323 | 1.226866e-18 |
| Aldoc | 7.569245e-22 | 0.8741418 | 0.812 | 0.158 | 1.529744e-17 |
| Dmrt2 | 1.083406e-21 | 0.6631530 | 0.500 | 0.015 | 2.189563e-17 |
| H2afy2 | 4.241098e-18 | 0.9311086 | 1.000 | 0.798 | 8.571258e-14 |
| Stra6 | 1.997664e-17 | 0.6158239 | 0.844 | 0.161 | 4.037278e-13 |
| Wfdc21 | 1.134869e-15 | 0.6485206 | 0.438 | 0.023 | 2.293570e-11 |
| Arl4a | 2.918290e-15 | 1.0483502 | 0.844 | 0.410 | 5.897863e-11 |
| Net1 | 1.263320e-14 | 0.6870002 | 0.844 | 0.469 | 2.553170e-10 |
| Plppr1 | 1.824226e-14 | 0.5375180 | 0.500 | 0.063 | 3.686761e-10 |
| Fbln1 | 2.879915e-14 | 0.8197909 | 1.000 | 0.768 | 5.820308e-10 |
| Halr1 | 3.558487e-14 | 0.4140141 | 0.469 | 0.030 | 7.191703e-10 |
| Foxc1 | 1.601517e-13 | 0.5404909 | 0.750 | 0.149 | 3.236666e-09 |
| Fam64a | 4.065572e-13 | 0.8244761 | 1.000 | 0.680 | 8.216521e-09 |
| Nkx3-1 | 5.226621e-13 | 0.5054500 | 0.406 | 0.025 | 1.056300e-08 |
| Dact1 | 5.230640e-13 | 0.6231829 | 0.625 | 0.194 | 1.057112e-08 |
| Ncam1 | 7.891315e-13 | 0.7912532 | 0.719 | 0.345 | 1.594835e-08 |
| Fstl1 | 2.470688e-12 | 0.7505362 | 0.969 | 0.684 | 4.993261e-08 |
| 2610016A17Rik | 6.829975e-12 | 0.2667935 | 0.281 | 0.009 | 1.380338e-07 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Prpf4 | 0.008810417 | 0.2530831 | 0.750 | 0.625 | 1 |
| Trim37 | 0.009043340 | 0.2560223 | 0.625 | 0.438 | 1 |
| Ywhaq | 0.009068746 | 0.2687471 | 1.000 | 0.999 | 1 |
| Csnk2a1 | 0.009524064 | 0.2762279 | 0.875 | 0.729 | 1 |
| Hmgn3 | 0.009649909 | 0.3190608 | 0.656 | 0.475 | 1 |
| Tpm3-rs7 | 0.009873854 | 0.3554377 | 0.844 | 0.724 | 1 |
| Dbn1 | 0.009928132 | 0.2535965 | 0.938 | 0.753 | 1 |
| Sept6 | 0.010394636 | 0.2823520 | 0.719 | 0.495 | 1 |
| Sepw1 | 0.010420293 | 0.2643817 | 1.000 | 0.988 | 1 |
| Prim1 | 0.012385129 | 0.2787811 | 0.812 | 0.693 | 1 |
| Igf2bp3 | 0.012625630 | 0.2720113 | 0.938 | 0.889 | 1 |
| Knop1 | 0.013098526 | 0.2826492 | 0.969 | 0.936 | 1 |
| Mettl9 | 0.014001443 | 0.2648959 | 1.000 | 0.932 | 1 |
| Anxa5 | 0.015710636 | 0.3289497 | 0.625 | 0.431 | 1 |
| Mfap2 | 0.017547656 | 0.2522632 | 0.719 | 0.501 | 1 |
| Btf3l4 | 0.018406911 | 0.2766930 | 1.000 | 0.946 | 1 |
| Eno1b | 0.018665459 | 0.5890729 | 0.812 | 0.774 | 1 |
| Hnrnph3 | 0.019288543 | 0.2767326 | 0.969 | 0.920 | 1 |
| Parl | 0.021363097 | 0.2536619 | 0.969 | 0.910 | 1 |
| Marcksl1 | 0.021663885 | 0.2752344 | 1.000 | 0.988 | 1 |
| Fbxo5 | 0.021678896 | 0.2592510 | 0.781 | 0.739 | 1 |
| Igf2bp2 | 0.021775718 | 0.2556107 | 0.875 | 0.808 | 1 |
| Srsf10 | 0.022323107 | 0.2652638 | 1.000 | 0.971 | 1 |
| Tyms | 0.023164208 | 0.2611897 | 1.000 | 0.938 | 1 |
| Clns1a | 0.024441051 | 0.2543720 | 1.000 | 0.973 | 1 |
| Hdgfrp3 | 0.024972584 | 0.2592345 | 0.688 | 0.493 | 1 |
| Mcm6 | 0.027792270 | 0.2554617 | 0.938 | 0.927 | 1 |
| E130012A19Rik | 0.031196912 | 0.2505807 | 0.906 | 0.742 | 1 |
| Ctbp2 | 0.041899163 | 0.2596800 | 0.844 | 0.746 | 1 |
| Hist1h2ap | 0.060531641 | 0.2521073 | 0.844 | 0.815 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Tbx18 | 0.000000e+00 | 1.1690860 | 1.000 | 0.027 | 0.000000e+00 |
| Meox1 | 1.608688e-225 | 2.6724839 | 1.000 | 0.125 | 3.251159e-221 |
| Tcf15 | 1.679888e-194 | 2.8553316 | 0.981 | 0.139 | 3.395053e-190 |
| Arg1 | 3.056213e-175 | 2.4560501 | 1.000 | 0.238 | 6.176607e-171 |
| Aldh1a2 | 1.281680e-141 | 2.3237116 | 0.923 | 0.232 | 2.590276e-137 |
| Foxc2 | 4.230369e-141 | 1.3493483 | 0.917 | 0.135 | 8.549575e-137 |
| Ppp1r1a | 2.414083e-108 | 1.2658066 | 0.904 | 0.372 | 4.878863e-104 |
| Fbn2 | 3.751131e-103 | 1.1423702 | 0.923 | 0.401 | 7.581036e-99 |
| Cadm1 | 2.449860e-92 | 1.0705054 | 0.987 | 0.684 | 4.951166e-88 |
| Fst | 2.521423e-89 | 1.7622669 | 0.904 | 0.319 | 5.095795e-85 |
| Foxc1 | 5.745875e-77 | 0.6740579 | 0.788 | 0.145 | 1.161241e-72 |
| Anxa5 | 2.855667e-74 | 1.1991896 | 0.878 | 0.427 | 5.771303e-70 |
| Meox2 | 1.982898e-72 | 0.3565805 | 0.391 | 0.011 | 4.007438e-68 |
| Foxd2 | 6.985548e-70 | 0.4448116 | 0.487 | 0.032 | 1.411779e-65 |
| Traf3ip2 | 7.841269e-70 | 0.5272081 | 0.519 | 0.090 | 1.584720e-65 |
| Halr1 | 1.144598e-67 | 0.4334534 | 0.468 | 0.027 | 2.313232e-63 |
| Six1 | 6.471516e-66 | 0.6279574 | 0.712 | 0.119 | 1.307893e-61 |
| Foxd1 | 4.168532e-65 | 0.5129673 | 0.571 | 0.061 | 8.424603e-61 |
| Marcks | 4.550540e-65 | 1.0658904 | 1.000 | 0.824 | 9.196641e-61 |
| H2afy2 | 1.395941e-64 | 0.8279321 | 1.000 | 0.797 | 2.821196e-60 |
| Stra6 | 8.919022e-63 | 0.6044083 | 0.699 | 0.158 | 1.802534e-58 |
| Meis2 | 3.499667e-62 | 0.9807898 | 0.974 | 0.613 | 7.072826e-58 |
| Nkx3-1 | 1.972823e-61 | 0.5408431 | 0.391 | 0.022 | 3.987074e-57 |
| Wfdc21 | 1.634859e-59 | 0.4556079 | 0.397 | 0.020 | 3.304050e-55 |
| Ntn1 | 2.801895e-58 | 0.4894283 | 0.660 | 0.120 | 5.662629e-54 |
| Gstm5 | 8.103221e-58 | 0.9095952 | 0.949 | 0.797 | 1.637661e-53 |
| Dmrt2 | 1.826602e-55 | 0.3642579 | 0.340 | 0.014 | 3.691562e-51 |
| Itih5 | 4.905517e-55 | 0.4135459 | 0.462 | 0.055 | 9.914051e-51 |
| Pax3 | 1.762601e-53 | 0.5814096 | 0.724 | 0.160 | 3.562217e-49 |
| Ptn | 1.408892e-51 | 1.0248500 | 1.000 | 0.593 | 2.847371e-47 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Dera | 8.001886e-11 | 0.2736740 | 0.795 | 0.702 | 1.617181e-06 |
| Lmo4 | 8.127668e-11 | 0.3327732 | 0.827 | 0.630 | 1.642602e-06 |
| Capn2 | 8.462473e-11 | 0.2608723 | 0.622 | 0.370 | 1.710266e-06 |
| Ctxn1 | 1.036404e-10 | 0.2642595 | 0.635 | 0.398 | 2.094573e-06 |
| Dkc1 | 1.314692e-10 | 0.3118558 | 0.994 | 0.939 | 2.656993e-06 |
| Rbmxl1 | 1.330521e-10 | 0.2674300 | 1.000 | 0.976 | 2.688984e-06 |
| Nmral1 | 1.532121e-10 | 0.2930492 | 0.910 | 0.772 | 3.096417e-06 |
| Ctnnb1 | 4.147727e-10 | 0.2663098 | 0.994 | 0.951 | 8.382556e-06 |
| Ptov1 | 5.922451e-10 | 0.2842163 | 1.000 | 0.985 | 1.196927e-05 |
| Smarcc1 | 6.116193e-10 | 0.2836756 | 0.994 | 0.949 | 1.236083e-05 |
| Hnrnph2 | 6.939997e-10 | 0.2612093 | 0.897 | 0.764 | 1.402573e-05 |
| Itm2c | 7.845105e-10 | 0.2740848 | 0.859 | 0.674 | 1.585496e-05 |
| Anp32e | 9.861255e-10 | 0.2724149 | 1.000 | 0.976 | 1.992960e-05 |
| Dcakd | 1.498862e-09 | 0.2681954 | 0.987 | 0.916 | 3.029200e-05 |
| E130012A19Rik | 1.620205e-09 | 0.2911799 | 0.910 | 0.741 | 3.274435e-05 |
| Snai1 | 2.085081e-09 | 0.2705949 | 0.538 | 0.296 | 4.213948e-05 |
| Cnot2 | 2.092982e-09 | 0.2500225 | 0.808 | 0.667 | 4.229916e-05 |
| Fign | 2.115612e-09 | 0.2503556 | 0.603 | 0.379 | 4.275652e-05 |
| Hsd17b10 | 2.206670e-09 | 0.2685410 | 0.981 | 0.934 | 4.459680e-05 |
| Tfpi | 2.221812e-09 | 0.2729094 | 0.494 | 0.265 | 4.490282e-05 |
| Arl3 | 4.897549e-09 | 0.2760108 | 0.936 | 0.868 | 9.897947e-05 |
| Acvr2b | 9.233405e-09 | 0.2549245 | 0.897 | 0.738 | 1.866071e-04 |
| Tbl1x | 9.487821e-09 | 0.2605653 | 0.904 | 0.817 | 1.917489e-04 |
| Malat1 | 1.344752e-08 | 0.3164166 | 1.000 | 0.998 | 2.717744e-04 |
| Notch1 | 7.186521e-08 | 0.2637782 | 0.385 | 0.199 | 1.452396e-03 |
| Tubb2b | 8.103651e-08 | 0.3203554 | 0.821 | 0.636 | 1.637748e-03 |
| Eva1b | 4.716110e-07 | 0.2840800 | 0.628 | 0.439 | 9.531259e-03 |
| Supt16 | 1.146949e-06 | 0.2558366 | 0.923 | 0.916 | 2.317984e-02 |
| Laptm4b | 8.228025e-06 | 0.2819543 | 0.814 | 0.702 | 1.662884e-01 |
| Cyr61 | 2.143855e-05 | 0.3226987 | 0.795 | 0.641 | 4.332731e-01 |
FeaturePlot(Emb8, features = ('Mesp2'))
FeaturePlot(Emb8, features = ('Ripply2'))
psm.g <- subset(x = Emb8, subset = Mesp2 > 0.1 | Ripply2 > 0.1)
psm.g
An object of class Seurat 20210 features across 415 samples within 1 assay Active assay: RNA (20210 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap
psm.g <- NormalizeData(psm.g, verbose = TRUE)
psm.g <- FindVariableFeatures(psm.g, verbose = TRUE)
psm.g <- ScaleData(psm.g, verbose = TRUE)
psm.g <- RunPCA(psm.g, features = VariableFeatures(object = psm.g), verbose = TRUE)
psm.g <- FindNeighbors(psm.g, dims = 1:20)
psm.g <- FindClusters(psm.g, resolution = 0.2)
psm.g <- RunUMAP(psm.g, dims = 1:20)
Centering and scaling data matrix PC_ 1 Positive: Pcdh19, Aldh1a2, Ifitm1, Dll3, Arg1, Lef1, Hoxb1, Meox1, Smoc1, Cadm1 Tcf15, Foxc2, Aldoc, Hoxd4, Dll1, Crabp1, Rbp1, Foxc1, Fabp5, Ptn Notch1, Magi1, Hoxb4, Rspo3, Foxb1, Laptm4b, Meis2, Ncam1, Cdx1, Epha4 Negative: Cryab, Crip2, Krt8, Nexn, Cdkn1c, Acta2, Car4, Ramp1, Krt18, Popdc2 Ppic, Sparc, Tnnt2, Gata5, Tagln, Csrp2, Sh3bgr, Tbx20, Myl7, Nebl Myl9, Tnnc1, Tnni1, H19, S100a10, Myl3, Dok4, Asb4, Cnn2, Podxl PC_ 2 Positive: Actc1, Myl4, Tnnt2, Asb2, Myl7, Fbxl22, Sh3bgr, Ramp1, Tnni1, Ttn Smyd1, Ankrd1, Csrp3, Tnnc1, Unc45b, RP23-455C13.7, Myh7, Nebl, Hspb2, Lmod1 Mef2c, Tmsb10, Cap2, Rassf5, Apobec2, Mybpc3, Hspb7, Ccdc141, Pgam2, Acta1 Negative: Slc2a3, Folr1, Reep6, Rbp4, Amn, Trap1a, Spint2, Mt1, Cldn6, Cystm1 Clic6, 2610528J11Rik, Apob, Cldn7, 2210011C24Rik, Apom, Epcam, Ctsh, Cubn, Bex4 Apoa4, Apoc1, S100g, Slc16a1, Pla2g12b, Pdzk1, Lgals2, Spink1, Xlr4b, Ttr PC_ 3 Positive: Actg1, Wbp5, Tmsb10, Tuba1a, Serpinh1, Ngfrap1, Marcks, Tpm4, Actb, Hmga2 Krt18, Basp1, Meg3, Lin28a, Slc9a3r1, Hspa5, Calm2, Sox4, Hsp90b1, Tmsb4x Ccnd1, Rbp1, Nnat, Mogat2, Cmtm7, Sox11, Cald1, Rala, Krt8, Fermt2 Negative: Cited4, Klf1, Snca, Gypa, Slc38a5, Hba-a1, Hbb-bh1, Smim1, Hba-x, Blvrb Alas2, Gata1, Gypc, Tspan32, Hba-a2, Trim10, Epor, Spns2, Epb42, Nfe2 Gfi1b, Hbb-y, Sptb, Cpox, Ube2l6, Chchd10, Fermt3, Tmem56, Slc25a37, Car2 PC_ 4 Positive: Fn1, H19, Aldh1a2, Pcdh19, Rasgrp3, Arg1, Ifitm1, Foxc2, Tcf15, Meox1 Fam212a, Foxc1, Gpc3, Smoc1, Cald1, Notch1, Cited1, Tfpi, Tspan9, Laptm4b Igf2, Aldoc, Anxa5, Fth1, Vim, Dlk1, Dusp9, Gstm5, Ftl1, Epb41l3 Negative: Crabp2, Sox2, Stmn2, Ldha, Map1b, Sfrp1, Pcsk1n, Igfbp2, Hes3, Otx2 Pls3, Eras, Pim2, Tpi1, Bex4, Sfrp2, Slc2a1, Sox1, Cntfr, Sp8 Uchl1, Slc2a3, Hes5, Pfkl, H2-D1, Wscd1, Wfdc2, Tspan13, Lhx5, Pou3f1 PC_ 5 Positive: Cubn, Folr1, Amn, 2610528J11Rik, Ttr, Lgals2, Apom, Rbp4, 2210011C24Rik, Clic6 Pdzk1, S100g, Apob, Apoc3, Apoa1, Fmr1nb, Apoc1, Apoa4, Slc39a5, Ctsh Xlr4b, Pla2g12b, Spink1, Reep6, Atp1b1, Smlr1, Rab11fip5, Gipc2, Rhox5, Tdh Negative: Mest, Ecscr, Rasip1, Igf1, Rhoj, Plvap, Flt4, She, Pmp22, Vim Esam, Exoc3l, Cldn5, Tgfb1, Ahnak, Hdgf, Sox18, Icam2, Tal1, Gja4 Hapln1, Elk3, Sh3bgrl3, Stard8, Vamp5, Akap12, Kdr, Acvrl1, Sdpr, Dlk1 Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 415 Number of edges: 11493 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.8803 Number of communities: 4 Elapsed time: 0 seconds
15:05:19 UMAP embedding parameters a = 0.9922 b = 1.112 15:05:19 Read 415 rows and found 20 numeric columns 15:05:19 Using Annoy for neighbor search, n_neighbors = 30 15:05:19 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 15:05:19 Writing NN index file to temp file /tmp/Rtmpvyz1TL/file1a7f03b93650 15:05:19 Searching Annoy index using 1 thread, search_k = 3000 15:05:19 Annoy recall = 100% 15:05:19 Commencing smooth kNN distance calibration using 1 thread 15:05:20 Initializing from normalized Laplacian + noise 15:05:20 Commencing optimization for 500 epochs, with 14514 positive edges 15:05:21 Optimization finished
DimPlot(psm.g, reduction = 'umap', label = T) #+ DarkTheme()
mes.g <- subset(x = psm.g, subset = Mesp2 > 0.5)
rip.g <- subset(x = psm.g, subset = Ripply2 > 0.5)
psm.g@meta.data$Mesp2 <-
ifelse(
rownames(psm.g@meta.data) %in% colnames(mes.g), "Mesp2", "")
psm.g@meta.data$Ripply2 <-
ifelse(
rownames(psm.g@meta.data) %in% colnames(rip.g), "Ripply2", "")
psm.g$PSMIdentity <- paste(psm.g$Mesp2,psm.g$Ripply2)#meox$None,meox$Both)
DimPlot(object = psm.g, group.by = 'PSMIdentity', pt.size = 1, cols = c("grey", "green", "blue", "red"))
psm.gg <- SetIdent(psm.g, value = psm.g@meta.data$PSMIdentity)
MRmarkers <- FindMarkers(psm.gg, ident.1 = "Mesp2 Ripply2", only.pos = TRUE)
Mmarkers <- FindMarkers(psm.gg, ident.1 = "Mesp2 ", only.pos = TRUE)
Rmarkers <- FindMarkers(psm.gg, ident.1 = " Ripply2", only.pos = TRUE)
MRmarkers
Mmarkers
Rmarkers
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Ripply2 | 1.099604e-17 | 2.3462136 | 1.000 | 0.427 | 2.222300e-13 |
| Pcdh8 | 2.986151e-16 | 1.2588198 | 0.885 | 0.201 | 6.035012e-12 |
| Mesp2 | 5.683400e-15 | 2.0199498 | 1.000 | 0.599 | 1.148615e-10 |
| Mesp1 | 4.441841e-14 | 1.6767957 | 0.885 | 0.260 | 8.976961e-10 |
| Lfng | 3.108859e-13 | 1.1860437 | 0.885 | 0.311 | 6.283003e-09 |
| Wfdc21 | 2.841726e-10 | 0.6804978 | 0.654 | 0.167 | 5.743127e-06 |
| Dll1 | 6.401879e-10 | 1.0976130 | 0.923 | 0.378 | 1.293820e-05 |
| Rtn1 | 8.647307e-10 | 0.6059097 | 0.885 | 0.362 | 1.747621e-05 |
| Meox1 | 1.908325e-09 | 1.2568787 | 0.923 | 0.460 | 3.856725e-05 |
| Foxc2 | 1.912414e-09 | 0.9833173 | 0.962 | 0.442 | 3.864988e-05 |
| Tcf15 | 2.685132e-09 | 1.1691937 | 0.962 | 0.445 | 5.426652e-05 |
| Slc38a8 | 3.291727e-09 | 0.2849476 | 0.385 | 0.059 | 6.652581e-05 |
| Aldh1a2 | 1.465903e-08 | 1.2290467 | 0.962 | 0.584 | 2.962590e-04 |
| Foxc1 | 2.298664e-08 | 0.7163392 | 0.885 | 0.416 | 4.645599e-04 |
| Pcdh19 | 2.901510e-08 | 1.0671261 | 0.923 | 0.514 | 5.863951e-04 |
| Efnb2 | 6.296645e-08 | 0.5534249 | 0.769 | 0.311 | 1.272552e-03 |
| Magi1 | 9.177440e-08 | 0.7326336 | 0.885 | 0.450 | 1.854761e-03 |
| Gadd45g | 2.246963e-07 | 0.9801916 | 0.846 | 0.455 | 4.541112e-03 |
| Ifitm1 | 3.030960e-07 | 0.8762290 | 1.000 | 0.956 | 6.125569e-03 |
| Zbtb18 | 3.100143e-07 | 0.4544024 | 0.615 | 0.213 | 6.265389e-03 |
| Dll3 | 3.703067e-07 | 0.7508126 | 0.962 | 0.530 | 7.483898e-03 |
| Hoxb1 | 4.075089e-07 | 0.7813852 | 1.000 | 0.643 | 8.235755e-03 |
| Notch1 | 4.327606e-07 | 0.8357473 | 0.885 | 0.468 | 8.746092e-03 |
| Smoc1 | 5.302011e-07 | 0.6513558 | 0.962 | 0.545 | 1.071536e-02 |
| Nrarp | 6.487043e-07 | 0.6177936 | 0.769 | 0.350 | 1.311031e-02 |
| Ptgds | 7.289973e-07 | 0.3679364 | 0.577 | 0.193 | 1.473304e-02 |
| Aldoc | 1.694064e-06 | 0.6699223 | 0.846 | 0.440 | 3.423703e-02 |
| Rasgrp3 | 2.050817e-06 | 0.6853901 | 0.769 | 0.393 | 4.144702e-02 |
| Twist1 | 2.056794e-06 | 0.4042977 | 0.731 | 0.296 | 4.156780e-02 |
| Camk2n1 | 3.039622e-06 | 0.4566990 | 0.615 | 0.242 | 6.143075e-02 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Pbx2 | 0.03700766 | 0.3052624 | 0.731 | 0.676 | 1 |
| B230219D22Rik | 0.03832816 | 0.3217128 | 0.769 | 0.681 | 1 |
| Ptms | 0.03932354 | 0.3399418 | 1.000 | 0.848 | 1 |
| Ap4m1 | 0.04042678 | 0.2721989 | 0.577 | 0.452 | 1 |
| Rps26-ps1 | 0.04109743 | 0.2742595 | 0.808 | 0.650 | 1 |
| Jam3 | 0.04132253 | 0.2613112 | 0.808 | 0.712 | 1 |
| Tardbp | 0.04193160 | 0.2836147 | 1.000 | 0.972 | 1 |
| Cachd1 | 0.04214845 | 0.2614122 | 0.577 | 0.463 | 1 |
| H1f0 | 0.04802688 | 0.4010000 | 0.923 | 0.869 | 1 |
| Copb1 | 0.04855608 | 0.2624915 | 0.692 | 0.697 | 1 |
| Sap30 | 0.05038704 | 0.3117547 | 0.962 | 0.895 | 1 |
| Cndp2 | 0.05526953 | 0.2686765 | 0.769 | 0.758 | 1 |
| Hmga2 | 0.05546752 | 0.2916082 | 1.000 | 0.954 | 1 |
| Ddah2 | 0.05722588 | 0.2752982 | 0.962 | 0.874 | 1 |
| Rbm39 | 0.05912637 | 0.2600526 | 1.000 | 1.000 | 1 |
| Scfd1 | 0.06287910 | 0.2667075 | 0.615 | 0.563 | 1 |
| Pafah1b3 | 0.06720621 | 0.2741242 | 0.923 | 0.920 | 1 |
| Jade1 | 0.07142777 | 0.3487293 | 0.654 | 0.602 | 1 |
| Add3 | 0.07692410 | 0.2658816 | 0.615 | 0.542 | 1 |
| Rit1 | 0.08533228 | 0.2745229 | 0.500 | 0.370 | 1 |
| Cep192 | 0.08534822 | 0.2702956 | 0.346 | 0.237 | 1 |
| Ntn1 | 0.09029999 | 0.2609528 | 0.423 | 0.301 | 1 |
| Cbx3 | 0.10508884 | 0.3172311 | 1.000 | 0.928 | 1 |
| Akirin2 | 0.11218825 | 0.3445079 | 0.885 | 0.895 | 1 |
| Rbm5 | 0.11950878 | 0.2724137 | 0.808 | 0.722 | 1 |
| Skap2 | 0.12449213 | 0.2714863 | 0.654 | 0.566 | 1 |
| Zeb2 | 0.13221540 | 0.3932342 | 0.692 | 0.663 | 1 |
| Amfr | 0.14011857 | 0.2540586 | 0.538 | 0.481 | 1 |
| Nnat | 0.25799819 | 0.2537601 | 0.885 | 0.866 | 1 |
| Pdcd4 | 0.36454841 | 0.2638481 | 0.500 | 0.504 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Mesp2 | 1.610327e-38 | 1.1074231 | 1.000 | 0.524 | 3.254471e-34 |
| Dll1 | 6.034764e-14 | 0.8331529 | 0.690 | 0.338 | 1.219626e-09 |
| Gpc3 | 6.715949e-13 | 0.6910036 | 0.954 | 0.872 | 1.357293e-08 |
| Mesp1 | 3.986364e-12 | 0.9107048 | 0.575 | 0.226 | 8.056442e-08 |
| Rspo3 | 3.114990e-11 | 0.8512076 | 0.816 | 0.558 | 6.295395e-07 |
| Cited1 | 6.366272e-11 | 0.5273543 | 0.931 | 0.686 | 1.286624e-06 |
| Pcdh19 | 1.703289e-10 | 0.6708032 | 0.828 | 0.463 | 3.442347e-06 |
| Ifitm1 | 2.299273e-10 | 0.6990887 | 1.000 | 0.948 | 4.646830e-06 |
| Smoc1 | 5.508674e-10 | 0.6778899 | 0.782 | 0.515 | 1.113303e-05 |
| Hoxb4 | 9.261608e-10 | 0.4604840 | 0.713 | 0.421 | 1.871771e-05 |
| Dll3 | 1.289107e-09 | 0.7980527 | 0.736 | 0.509 | 2.605285e-05 |
| Tbx6 | 1.443171e-09 | 0.5266777 | 0.621 | 0.320 | 2.916648e-05 |
| Kcnk3 | 2.689069e-08 | 0.3096960 | 0.356 | 0.119 | 5.434608e-04 |
| Foxb1 | 3.275748e-08 | 0.6027086 | 0.678 | 0.463 | 6.620287e-04 |
| Lhx1 | 3.376355e-08 | 0.4677318 | 0.402 | 0.155 | 6.823613e-04 |
| Prtg | 6.866978e-08 | 0.3988584 | 0.977 | 0.976 | 1.387816e-03 |
| Hoxb5os | 1.617373e-07 | 0.4330656 | 0.632 | 0.366 | 3.268711e-03 |
| Cdx1 | 2.356038e-07 | 0.4856338 | 0.828 | 0.595 | 4.761553e-03 |
| mt-Co2 | 3.971224e-07 | 0.3654614 | 0.989 | 0.997 | 8.025843e-03 |
| Arg1 | 4.079337e-07 | 0.5098013 | 0.828 | 0.524 | 8.244341e-03 |
| Aldh1a2 | 4.582536e-07 | 0.5929631 | 0.828 | 0.549 | 9.261305e-03 |
| mt-Atp6 | 9.393568e-07 | 0.2988385 | 1.000 | 1.000 | 1.898440e-02 |
| Nrarp | 1.173587e-06 | 0.4101149 | 0.563 | 0.326 | 2.371819e-02 |
| H2afy | 1.190116e-06 | 0.3765208 | 0.977 | 0.963 | 2.405224e-02 |
| Cadm1 | 1.201293e-06 | 0.3803477 | 0.931 | 0.860 | 2.427814e-02 |
| mt-Nd1 | 1.414153e-06 | 0.3402143 | 1.000 | 0.991 | 2.858003e-02 |
| Hoxb1 | 2.242645e-06 | 0.4904212 | 0.816 | 0.625 | 4.532386e-02 |
| Aldoc | 4.008577e-06 | 0.3778352 | 0.655 | 0.415 | 8.101333e-02 |
| Hoxa5 | 4.183310e-06 | 0.3374388 | 0.425 | 0.232 | 8.454470e-02 |
| Hoxb2 | 4.797881e-06 | 0.3381909 | 0.805 | 0.649 | 9.696518e-02 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| mt-Nd2 | 3.662957e-05 | 0.2660083 | 0.977 | 0.979 | 0.7402837 |
| Ddit4 | 3.764557e-05 | 0.4125056 | 0.667 | 0.503 | 0.7608170 |
| Hmga2 | 4.375618e-05 | 0.2555536 | 0.954 | 0.957 | 0.8843125 |
| Pcolce2 | 4.810080e-05 | 0.3515316 | 0.460 | 0.284 | 0.9721173 |
| Hoxd4 | 5.444518e-05 | 0.2707253 | 0.575 | 0.375 | 1.0000000 |
| mt-Nd4 | 6.777180e-05 | 0.2632293 | 1.000 | 0.997 | 1.0000000 |
| 2810474O19Rik | 6.849023e-05 | 0.3160216 | 0.713 | 0.613 | 1.0000000 |
| Meox1 | 7.236285e-05 | 0.3347365 | 0.678 | 0.439 | 1.0000000 |
| Maml3 | 8.054522e-05 | 0.3008112 | 0.529 | 0.369 | 1.0000000 |
| Greb1l | 4.047405e-04 | 0.2831423 | 0.805 | 0.716 | 1.0000000 |
| Foxc2 | 4.632993e-04 | 0.2960285 | 0.644 | 0.430 | 1.0000000 |
| Arl4a | 4.688460e-04 | 0.2977531 | 0.621 | 0.488 | 1.0000000 |
| Fn1 | 5.484890e-04 | 0.2841546 | 0.897 | 0.814 | 1.0000000 |
| Rbp1 | 5.882688e-04 | 0.2917548 | 0.954 | 0.909 | 1.0000000 |
| Foxc1 | 7.691644e-04 | 0.2934193 | 0.563 | 0.415 | 1.0000000 |
| Tubb2b | 8.530186e-04 | 0.2762122 | 0.851 | 0.857 | 1.0000000 |
| Slc9a3r1 | 1.056029e-03 | 0.2597353 | 0.839 | 0.872 | 1.0000000 |
| Snai1 | 1.209647e-03 | 0.2880872 | 0.632 | 0.491 | 1.0000000 |
| Hoxa1 | 1.368710e-03 | 0.2768718 | 0.690 | 0.537 | 1.0000000 |
| Gadd45g | 1.464025e-03 | 0.4229750 | 0.575 | 0.454 | 1.0000000 |
| E130012A19Rik | 2.975747e-03 | 0.2866473 | 0.828 | 0.841 | 1.0000000 |
| Osr1 | 3.550525e-03 | 0.3279883 | 0.310 | 0.186 | 1.0000000 |
| F11r | 8.423543e-03 | 0.2833314 | 0.598 | 0.527 | 1.0000000 |
| Smc6 | 1.041848e-02 | 0.2930710 | 0.931 | 0.939 | 1.0000000 |
| Hbb-bs | 1.226296e-02 | 0.4496396 | 0.023 | 0.113 | 1.0000000 |
| Meis1 | 1.905271e-02 | 0.2622255 | 0.540 | 0.473 | 1.0000000 |
| Ypel3 | 4.879550e-02 | 0.2655758 | 0.506 | 0.512 | 1.0000000 |
| Hist1h2ap | 5.760238e-02 | 0.3377607 | 0.816 | 0.845 | 1.0000000 |
| Gm10116 | 2.643018e-01 | 0.2591244 | 0.805 | 0.866 | 1.0000000 |
| H1f0 | 4.169543e-01 | 0.2522098 | 0.805 | 0.890 | 1.0000000 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Ripply2 | 6.206677e-31 | 1.3898025 | 1.000 | 0.382 | 1.254370e-26 |
| Pcdh8 | 3.903461e-16 | 1.0622405 | 0.648 | 0.183 | 7.888895e-12 |
| Fam101a | 1.583541e-14 | 0.2706580 | 0.352 | 0.044 | 3.200336e-10 |
| Cer1 | 7.924666e-10 | 0.9311828 | 0.426 | 0.114 | 1.601575e-05 |
| Six1 | 2.369211e-08 | 0.4623788 | 0.574 | 0.252 | 4.788176e-04 |
| Wnt16 | 3.113905e-08 | 0.2788747 | 0.333 | 0.080 | 6.293201e-04 |
| Tbx18 | 1.482047e-07 | 0.3555626 | 0.333 | 0.091 | 2.995218e-03 |
| Gstm5 | 3.522308e-07 | 0.8305118 | 0.907 | 0.903 | 7.118585e-03 |
| Lrrtm3 | 5.831331e-07 | 0.3097058 | 0.389 | 0.130 | 1.178512e-02 |
| Nbl1 | 7.573693e-07 | 0.3151907 | 0.370 | 0.119 | 1.530643e-02 |
| Sema6a | 1.154931e-06 | 0.4663920 | 0.759 | 0.501 | 2.334115e-02 |
| Notch1 | 1.232678e-06 | 0.7205186 | 0.685 | 0.465 | 2.491242e-02 |
| Synm | 3.419128e-06 | 0.2930793 | 0.500 | 0.211 | 6.910057e-02 |
| Tead2 | 7.890255e-06 | 0.3344993 | 0.981 | 0.958 | 1.594621e-01 |
| Camk2n1 | 1.117100e-05 | 0.3160627 | 0.481 | 0.233 | 2.257660e-01 |
| Lef1 | 1.322848e-05 | 0.8201881 | 0.796 | 0.695 | 2.673476e-01 |
| Sfpq | 1.647001e-05 | 0.3649684 | 1.000 | 1.000 | 3.328589e-01 |
| Fgfr1 | 2.369779e-05 | 0.4820718 | 0.796 | 0.762 | 4.789323e-01 |
| Ppp1r1a | 2.822502e-05 | 0.6636466 | 0.796 | 0.657 | 5.704277e-01 |
| Wfdc21 | 2.971995e-05 | 0.4755887 | 0.389 | 0.169 | 6.006403e-01 |
| Meox1 | 4.034214e-05 | 0.4757516 | 0.722 | 0.454 | 8.153147e-01 |
| Epha4 | 5.609725e-05 | 0.4862350 | 0.685 | 0.593 | 1.000000e+00 |
| Zic2 | 5.844550e-05 | 0.2629225 | 0.444 | 0.216 | 1.000000e+00 |
| Cadm1 | 6.158261e-05 | 0.4748955 | 0.926 | 0.867 | 1.000000e+00 |
| Rnd3 | 8.337831e-05 | 0.3799001 | 0.574 | 0.357 | 1.000000e+00 |
| Ssb | 9.359529e-05 | 0.2604201 | 1.000 | 0.997 | 1.000000e+00 |
| Dkc1 | 1.287716e-04 | 0.3358481 | 1.000 | 0.972 | 1.000000e+00 |
| Pax3 | 1.448067e-04 | 0.2503030 | 0.519 | 0.283 | 1.000000e+00 |
| B230219D22Rik | 1.733805e-04 | 0.2717987 | 0.796 | 0.670 | 1.000000e+00 |
| Peli2 | 2.060956e-04 | 0.2603627 | 0.574 | 0.371 | 1.000000e+00 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Ntn1 | 0.0008275038 | 0.2678811 | 0.463 | 0.285 | 1 |
| Epb41l3 | 0.0010371414 | 0.3106601 | 0.630 | 0.435 | 1 |
| Capzb | 0.0013404894 | 0.2532835 | 0.963 | 0.992 | 1 |
| Ythdf2 | 0.0014613238 | 0.2704530 | 0.944 | 0.875 | 1 |
| Smoc1 | 0.0015479943 | 0.4231681 | 0.704 | 0.551 | 1 |
| Top1 | 0.0020486529 | 0.2563324 | 1.000 | 1.000 | 1 |
| Ccdc186 | 0.0021893430 | 0.2517775 | 0.537 | 0.377 | 1 |
| Dll3 | 0.0022171418 | 0.2920902 | 0.722 | 0.532 | 1 |
| Gas1 | 0.0025488318 | 0.4633576 | 0.611 | 0.510 | 1 |
| Zic3 | 0.0028245461 | 0.2829788 | 0.500 | 0.338 | 1 |
| Mgea5 | 0.0028928958 | 0.2669638 | 0.685 | 0.596 | 1 |
| Dlc1 | 0.0035483074 | 0.2856088 | 0.481 | 0.346 | 1 |
| Hoxd1 | 0.0036738440 | 0.3605823 | 0.444 | 0.296 | 1 |
| Apip | 0.0037377805 | 0.3684229 | 0.704 | 0.687 | 1 |
| Luc7l | 0.0041826098 | 0.2591571 | 0.907 | 0.878 | 1 |
| H2afy2 | 0.0043246800 | 0.2628678 | 0.926 | 0.945 | 1 |
| Hoxb1 | 0.0055089251 | 0.2790023 | 0.778 | 0.648 | 1 |
| Fgf18 | 0.0073309583 | 0.3673355 | 0.407 | 0.255 | 1 |
| Hp1bp3 | 0.0087155199 | 0.2820125 | 0.685 | 0.573 | 1 |
| Add3 | 0.0089675999 | 0.2529385 | 0.648 | 0.532 | 1 |
| Prpf38b | 0.0093278092 | 0.2534699 | 0.981 | 0.936 | 1 |
| Cited2 | 0.0094821274 | 0.3689583 | 0.981 | 0.956 | 1 |
| Gm10709 | 0.0114777455 | 0.5253513 | 0.778 | 0.723 | 1 |
| Rasgrp3 | 0.0188249129 | 0.2743949 | 0.537 | 0.399 | 1 |
| Sox4 | 0.0204485721 | 0.2776426 | 0.889 | 0.898 | 1 |
| Foxp1 | 0.0357418654 | 0.3049478 | 0.778 | 0.767 | 1 |
| Rtn1 | 0.0358434165 | 0.3072820 | 0.463 | 0.385 | 1 |
| Hmgn3 | 0.0559015907 | 0.2926650 | 0.685 | 0.657 | 1 |
| Ifitm1 | 0.0654205653 | 0.2943302 | 0.889 | 0.970 | 1 |
| Phlda1 | 0.0913262741 | 0.3020221 | 0.630 | 0.601 | 1 |
emb8@meta.data$Mesp2 <-
ifelse(
rownames(emb8@meta.data) %in% colnames(mes.g), "Mesp2", "")
emb8@meta.data$Ripply2 <-
ifelse(
rownames(emb8@meta.data) %in% colnames(rip.g), "Ripply2", "")
emb8$PSMIdentity <- paste(emb8$Mesp2,emb8$Ripply2)
DimPlot(object = emb8, group.by = 'PSMIdentity', pt.size = 1, cols = c("grey", "green", "blue", "red"))
psm.gg <- SetIdent(emb8, value = emb8@meta.data$PSMIdentity)
MRmarkers <- FindMarkers(psm.gg, ident.1 = "Mesp2 Ripply2", only.pos = TRUE)
Mmarkers <- FindMarkers(psm.gg, ident.1 = "Mesp2 ", only.pos = TRUE)
Rmarkers <- FindMarkers(psm.gg, ident.1 = " Ripply2", only.pos = TRUE)
MRmarkers
Mmarkers
Rmarkers
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Mesp2 | 0.000000e+00 | 2.8055886 | 1.000 | 0.012 | 0.000000e+00 |
| Ripply2 | 0.000000e+00 | 2.9780430 | 1.000 | 0.008 | 0.000000e+00 |
| Pcdh8 | 9.330022e-171 | 1.6797153 | 0.885 | 0.025 | 1.885598e-166 |
| Wfdc21 | 4.301607e-102 | 0.8917273 | 0.654 | 0.023 | 8.693548e-98 |
| Slc38a8 | 2.635165e-97 | 0.3374166 | 0.385 | 0.008 | 5.325668e-93 |
| Mesp1 | 2.341107e-91 | 2.2947704 | 0.885 | 0.048 | 4.731378e-87 |
| Fgf18 | 1.935426e-80 | 0.7858936 | 0.654 | 0.029 | 3.911495e-76 |
| Ptgds | 6.281719e-50 | 0.5264488 | 0.577 | 0.036 | 1.269535e-45 |
| Dll1 | 3.280482e-49 | 1.6686426 | 0.923 | 0.101 | 6.629855e-45 |
| Pcdh19 | 1.367571e-42 | 2.2341247 | 0.923 | 0.125 | 2.763861e-38 |
| Foxc2 | 2.713593e-38 | 1.5140497 | 0.962 | 0.140 | 5.484172e-34 |
| Tcf15 | 5.017221e-38 | 2.2633785 | 0.962 | 0.144 | 1.013980e-33 |
| Meox1 | 5.514191e-38 | 2.0443925 | 0.923 | 0.131 | 1.114418e-33 |
| Lfng | 2.541621e-37 | 1.4691566 | 0.885 | 0.127 | 5.136615e-33 |
| Synm | 1.500091e-36 | 0.5296317 | 0.500 | 0.037 | 3.031683e-32 |
| Dll3 | 2.599427e-35 | 1.4826527 | 0.962 | 0.154 | 5.253441e-31 |
| Tbx6 | 8.463577e-34 | 0.8124156 | 0.769 | 0.089 | 1.710489e-29 |
| Foxc1 | 8.937457e-31 | 1.0763952 | 0.885 | 0.149 | 1.806260e-26 |
| Nap1l5 | 2.737045e-27 | 0.3923546 | 0.423 | 0.035 | 5.531568e-23 |
| Aldh1a2 | 3.316641e-27 | 2.6849816 | 0.962 | 0.236 | 6.702932e-23 |
| Aldoc | 8.291952e-27 | 1.1164915 | 0.846 | 0.158 | 1.675804e-22 |
| Magi1 | 2.433392e-25 | 1.1218554 | 0.885 | 0.191 | 4.917885e-21 |
| Smoc1 | 2.982572e-24 | 1.2797181 | 0.962 | 0.247 | 6.027779e-20 |
| Kcnk3 | 2.986738e-24 | 0.3056795 | 0.385 | 0.032 | 6.036198e-20 |
| Notch1 | 8.774010e-24 | 1.3095289 | 0.885 | 0.200 | 1.773227e-19 |
| Rtn1 | 3.493256e-23 | 0.8304308 | 0.885 | 0.203 | 7.059870e-19 |
| Zbtb18 | 1.702176e-22 | 0.5561240 | 0.615 | 0.090 | 3.440098e-18 |
| Hes7 | 3.703135e-22 | 1.1342390 | 0.808 | 0.154 | 7.484035e-18 |
| Lrrtm3 | 5.766806e-22 | 0.4196954 | 0.385 | 0.036 | 1.165471e-17 |
| Camk2n1 | 5.956538e-22 | 0.6004711 | 0.615 | 0.092 | 1.203816e-17 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Rnd3 | 0.01834505 | 0.2767770 | 0.538 | 0.353 | 1 |
| Tmeff1 | 0.01854252 | 0.2894304 | 0.769 | 0.641 | 1 |
| Mid1 | 0.02100115 | 0.2538585 | 0.731 | 0.600 | 1 |
| Mtf2 | 0.02193286 | 0.2620786 | 0.962 | 0.848 | 1 |
| Amotl2 | 0.02198977 | 0.3262231 | 0.615 | 0.464 | 1 |
| Atat1 | 0.02227056 | 0.2898008 | 0.500 | 0.359 | 1 |
| Ppp4r2 | 0.02308361 | 0.2564267 | 0.923 | 0.854 | 1 |
| H2afx | 0.02417147 | 0.2736247 | 0.962 | 0.973 | 1 |
| Tardbp | 0.02920124 | 0.2879345 | 1.000 | 0.971 | 1 |
| Mllt3 | 0.03071469 | 0.3339685 | 0.654 | 0.519 | 1 |
| Pdcd4 | 0.03158010 | 0.3481812 | 0.500 | 0.358 | 1 |
| Wtap | 0.03249125 | 0.2553423 | 0.846 | 0.838 | 1 |
| Tcf4 | 0.03256440 | 0.3295678 | 0.731 | 0.603 | 1 |
| Mex3a | 0.03324691 | 0.2748137 | 0.692 | 0.616 | 1 |
| Apex1 | 0.03480457 | 0.2717456 | 1.000 | 0.983 | 1 |
| Atxn7l3b | 0.03557138 | 0.2653213 | 1.000 | 0.976 | 1 |
| Bbx | 0.03705834 | 0.2619969 | 0.885 | 0.735 | 1 |
| Sh3bp1 | 0.04408644 | 0.2666500 | 0.654 | 0.622 | 1 |
| Akirin2 | 0.04462457 | 0.3987904 | 0.885 | 0.855 | 1 |
| Tle4 | 0.04649441 | 0.2500283 | 0.423 | 0.283 | 1 |
| Zfp280d | 0.05491594 | 0.2640043 | 0.538 | 0.429 | 1 |
| Rbm5 | 0.05911580 | 0.3030336 | 0.808 | 0.712 | 1 |
| Cep192 | 0.06389785 | 0.2679156 | 0.346 | 0.229 | 1 |
| Amfr | 0.06700236 | 0.2734601 | 0.538 | 0.436 | 1 |
| Rit1 | 0.07327172 | 0.2525821 | 0.500 | 0.357 | 1 |
| Cnot7 | 0.09291055 | 0.2714241 | 0.731 | 0.693 | 1 |
| Eif2a | 0.11436959 | 0.2542534 | 0.846 | 0.859 | 1 |
| Dhx36 | 0.11867882 | 0.2770987 | 0.731 | 0.674 | 1 |
| Ccnb2 | 0.20084846 | 0.2666239 | 0.846 | 0.865 | 1 |
| Hist1h2ap | 0.50333359 | 0.3144637 | 0.769 | 0.815 | 1 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Mesp2 | 0.000000e+00 | 1.8379829 | 1.000 | 0.009 | 0.000000e+00 |
| Mesp1 | 2.520548e-121 | 1.4743508 | 0.575 | 0.047 | 5.094027e-117 |
| Pcdh19 | 2.455023e-103 | 1.7730421 | 0.828 | 0.123 | 4.961602e-99 |
| Fgf18 | 3.604250e-90 | 0.4872355 | 0.391 | 0.028 | 7.284189e-86 |
| Dll1 | 3.694440e-85 | 1.2898249 | 0.690 | 0.099 | 7.466464e-81 |
| Tbx6 | 1.027943e-70 | 0.8130426 | 0.621 | 0.087 | 2.077473e-66 |
| Kcnk3 | 1.120521e-66 | 0.3892620 | 0.356 | 0.031 | 2.264573e-62 |
| Dll3 | 4.552606e-63 | 1.3894372 | 0.736 | 0.152 | 9.200816e-59 |
| Meox1 | 1.685991e-56 | 1.1677768 | 0.678 | 0.130 | 3.407389e-52 |
| Aldh1a2 | 8.148752e-55 | 2.0286109 | 0.828 | 0.235 | 1.646863e-50 |
| Synm | 6.188650e-54 | 0.3889836 | 0.345 | 0.036 | 1.250726e-49 |
| Tcf15 | 7.842621e-49 | 1.3455288 | 0.667 | 0.143 | 1.584994e-44 |
| Arg1 | 4.239309e-47 | 1.5921653 | 0.828 | 0.242 | 8.567643e-43 |
| Ptgds | 6.036787e-46 | 0.2925313 | 0.322 | 0.036 | 1.220035e-41 |
| Aldoc | 1.139201e-45 | 0.7914539 | 0.655 | 0.157 | 2.302326e-41 |
| Foxc2 | 3.525349e-45 | 0.8467239 | 0.644 | 0.139 | 7.124730e-41 |
| Smoc1 | 4.665243e-45 | 1.1914790 | 0.782 | 0.246 | 9.428456e-41 |
| Rspo3 | 1.564027e-37 | 1.3752640 | 0.816 | 0.307 | 3.160898e-33 |
| Hes7 | 2.013894e-37 | 0.9419586 | 0.632 | 0.153 | 4.070080e-33 |
| Ifitm1 | 7.459065e-37 | 1.6737503 | 1.000 | 0.833 | 1.507477e-32 |
| Pcdh8 | 5.792154e-33 | 0.2797022 | 0.230 | 0.025 | 1.170594e-28 |
| Foxc1 | 1.533117e-32 | 0.6452788 | 0.563 | 0.148 | 3.098429e-28 |
| Magi1 | 1.559419e-32 | 0.7155757 | 0.609 | 0.190 | 3.151586e-28 |
| Rnf213 | 1.917067e-32 | 0.3938864 | 0.414 | 0.079 | 3.874392e-28 |
| Hoxb1 | 2.357172e-32 | 1.0339709 | 0.816 | 0.324 | 4.763845e-28 |
| Hoxd4 | 4.899276e-32 | 0.5171666 | 0.575 | 0.149 | 9.901438e-28 |
| Lhx1 | 1.010630e-31 | 0.5473985 | 0.402 | 0.076 | 2.042484e-27 |
| Hoxb5os | 4.318888e-30 | 0.6964337 | 0.632 | 0.190 | 8.728472e-26 |
| Hoxb4 | 1.050095e-28 | 0.6470060 | 0.713 | 0.255 | 2.122243e-24 |
| Foxb1 | 1.440888e-27 | 0.7862231 | 0.678 | 0.232 | 2.912035e-23 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Clic4 | 8.306056e-06 | 0.2815203 | 0.839 | 0.764 | 0.1678654 |
| Dusp6 | 9.120965e-06 | 0.2692098 | 0.506 | 0.328 | 0.1843347 |
| mt-Co2 | 1.105314e-05 | 0.2604463 | 0.989 | 0.992 | 0.2233840 |
| Gm1673 | 1.153864e-05 | 0.2515925 | 0.609 | 0.446 | 0.2331959 |
| Kif5b | 1.595161e-05 | 0.3017168 | 0.977 | 0.967 | 0.3223821 |
| Tmem243 | 1.797231e-05 | 0.2978166 | 0.494 | 0.346 | 0.3632203 |
| Tead2 | 1.923663e-05 | 0.2683378 | 0.954 | 0.877 | 0.3887723 |
| Tmem176b | 2.462151e-05 | 0.2716651 | 0.425 | 0.270 | 0.4976007 |
| Rtn1 | 3.131769e-05 | 0.2559975 | 0.356 | 0.203 | 0.6329306 |
| Ypel3 | 3.134765e-05 | 0.3349067 | 0.506 | 0.363 | 0.6335361 |
| H1fx | 4.583759e-05 | 0.2544842 | 0.736 | 0.603 | 0.9263778 |
| Sap30 | 5.122165e-05 | 0.2840282 | 0.839 | 0.790 | 1.0000000 |
| Tia1 | 7.299079e-05 | 0.2538815 | 0.816 | 0.756 | 1.0000000 |
| Sept6 | 8.573903e-05 | 0.2802648 | 0.598 | 0.495 | 1.0000000 |
| Smc6 | 8.639439e-05 | 0.4127014 | 0.931 | 0.933 | 1.0000000 |
| Sh3bp1 | 8.995921e-05 | 0.2780763 | 0.724 | 0.621 | 1.0000000 |
| Calm3 | 9.006049e-05 | 0.2546301 | 0.977 | 0.981 | 1.0000000 |
| Lsm8 | 1.716737e-04 | 0.2611004 | 0.989 | 0.974 | 1.0000000 |
| Hes5 | 2.088051e-04 | 0.2614667 | 0.460 | 0.282 | 1.0000000 |
| Sh3bgrl | 2.248351e-04 | 0.2557246 | 0.747 | 0.661 | 1.0000000 |
| Hnrnpr | 2.502357e-04 | 0.2889358 | 0.977 | 0.966 | 1.0000000 |
| Rab34 | 2.952161e-04 | 0.2811760 | 0.782 | 0.686 | 1.0000000 |
| Mid1ip1 | 3.119322e-04 | 0.3072806 | 0.437 | 0.301 | 1.0000000 |
| Clk1 | 5.966937e-04 | 0.2583831 | 0.724 | 0.685 | 1.0000000 |
| Timm21 | 8.170278e-04 | 0.2595921 | 0.678 | 0.623 | 1.0000000 |
| F11r | 2.204332e-03 | 0.2694158 | 0.598 | 0.484 | 1.0000000 |
| Hist1h2ap | 4.046667e-03 | 0.4147944 | 0.816 | 0.815 | 1.0000000 |
| H1f0 | 5.515399e-03 | 0.4237704 | 0.805 | 0.819 | 1.0000000 |
| Rbm4b | 2.093001e-02 | 0.2613776 | 0.667 | 0.642 | 1.0000000 |
| Id1 | 3.292169e-01 | 0.3222890 | 0.816 | 0.847 | 1.0000000 |
| p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | |
|---|---|---|---|---|---|
| <dbl> | <dbl> | <dbl> | <dbl> | <dbl> | |
| Ripply2 | 0.000000e+00 | 2.0739576 | 1.000 | 0.007 | 0.000000e+00 |
| Pcdh8 | 2.595008e-187 | 1.4151802 | 0.648 | 0.025 | 5.244511e-183 |
| Synm | 1.136563e-73 | 0.4884045 | 0.500 | 0.036 | 2.296993e-69 |
| Wfdc21 | 3.078591e-72 | 0.6694911 | 0.389 | 0.022 | 6.221833e-68 |
| Fam101a | 1.370990e-65 | 0.2853534 | 0.352 | 0.020 | 2.770770e-61 |
| Fgf18 | 1.614408e-61 | 0.6441314 | 0.407 | 0.029 | 3.262718e-57 |
| Cer1 | 4.024789e-56 | 1.1593796 | 0.426 | 0.035 | 8.134098e-52 |
| Wnt16 | 2.270227e-50 | 0.3248976 | 0.333 | 0.023 | 4.588130e-46 |
| Lrrtm3 | 1.189816e-44 | 0.4087310 | 0.389 | 0.036 | 2.404618e-40 |
| Pcdh19 | 1.032601e-42 | 1.8244907 | 0.685 | 0.125 | 2.086887e-38 |
| Meox1 | 3.917352e-42 | 1.3120961 | 0.722 | 0.131 | 7.916969e-38 |
| Dll3 | 2.920930e-36 | 1.0436084 | 0.722 | 0.153 | 5.903200e-32 |
| Tcf15 | 3.356881e-36 | 1.5603283 | 0.704 | 0.144 | 6.784257e-32 |
| Tbx18 | 2.087345e-34 | 0.4135135 | 0.333 | 0.034 | 4.218525e-30 |
| Foxc2 | 6.406998e-33 | 0.9384111 | 0.667 | 0.139 | 1.294854e-28 |
| Ptgds | 1.107584e-27 | 0.2946468 | 0.315 | 0.036 | 2.238427e-23 |
| Notch1 | 1.378421e-26 | 1.1487828 | 0.685 | 0.199 | 2.785789e-22 |
| Six1 | 4.223446e-26 | 0.5816588 | 0.574 | 0.123 | 8.535584e-22 |
| Nbl1 | 2.253246e-25 | 0.3842720 | 0.370 | 0.054 | 4.553811e-21 |
| Agpat9 | 3.173140e-25 | 0.2764305 | 0.389 | 0.058 | 6.412917e-21 |
| Camk2n1 | 5.816581e-25 | 0.4483026 | 0.481 | 0.092 | 1.175531e-20 |
| Lfng | 1.931987e-23 | 0.5607554 | 0.556 | 0.126 | 3.904546e-19 |
| Aldh1a2 | 5.933447e-23 | 2.2874588 | 0.685 | 0.236 | 1.199150e-18 |
| Arg1 | 5.358969e-22 | 1.9083416 | 0.704 | 0.243 | 1.083048e-17 |
| Smoc1 | 1.608780e-21 | 1.0410531 | 0.704 | 0.247 | 3.251344e-17 |
| Ppp1r1a | 2.992536e-19 | 1.2516710 | 0.796 | 0.376 | 6.047915e-15 |
| Hes7 | 2.567842e-18 | 0.6385202 | 0.574 | 0.154 | 5.189608e-14 |
| Foxc1 | 4.777807e-18 | 0.5395474 | 0.537 | 0.149 | 9.655947e-14 |
| Aldoc | 5.019522e-18 | 0.6127552 | 0.537 | 0.158 | 1.014445e-13 |
| Hoxb1 | 6.174202e-18 | 0.8973299 | 0.778 | 0.325 | 1.247806e-13 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| Rtn4 | 0.0006768538 | 0.2827056 | 0.963 | 0.938 | 1 |
| Sall3 | 0.0006933867 | 0.2888976 | 0.519 | 0.339 | 1 |
| Rpf1 | 0.0007003878 | 0.2730716 | 0.926 | 0.834 | 1 |
| Hnrnph1 | 0.0008167911 | 0.2693229 | 1.000 | 0.989 | 1 |
| Atxn7l3b | 0.0008932777 | 0.2625642 | 1.000 | 0.976 | 1 |
| Dpysl5 | 0.0009268743 | 0.2557641 | 0.444 | 0.282 | 1 |
| Rbbp4 | 0.0009771107 | 0.2567275 | 1.000 | 0.977 | 1 |
| Mgea5 | 0.0011068675 | 0.2602528 | 0.685 | 0.563 | 1 |
| Tcf3 | 0.0011835730 | 0.2627652 | 0.944 | 0.880 | 1 |
| Zfp362 | 0.0011986542 | 0.2561798 | 0.352 | 0.206 | 1 |
| Acvr2b | 0.0015142548 | 0.2556448 | 0.815 | 0.739 | 1 |
| Zeb2 | 0.0017120921 | 0.2670225 | 0.611 | 0.470 | 1 |
| Pdcd2 | 0.0022569900 | 0.2512552 | 0.759 | 0.680 | 1 |
| Gjc1 | 0.0033052967 | 0.2980765 | 0.704 | 0.595 | 1 |
| Gpc3 | 0.0035246687 | 0.2610637 | 0.833 | 0.802 | 1 |
| Tgif1 | 0.0044307919 | 0.2767440 | 0.704 | 0.576 | 1 |
| Akirin2 | 0.0047479340 | 0.2605701 | 0.870 | 0.855 | 1 |
| Pdhx | 0.0049238084 | 0.3134310 | 0.500 | 0.399 | 1 |
| Prpf38b | 0.0061284109 | 0.2553665 | 0.981 | 0.922 | 1 |
| Arl2bp | 0.0076423268 | 0.2602202 | 0.889 | 0.838 | 1 |
| Enah | 0.0077585861 | 0.2886550 | 0.778 | 0.736 | 1 |
| Gm1673 | 0.0078570195 | 0.2597286 | 0.574 | 0.446 | 1 |
| Gm10709 | 0.0086128894 | 0.4951342 | 0.778 | 0.693 | 1 |
| Ccnd1 | 0.0092856433 | 0.2848468 | 0.815 | 0.723 | 1 |
| Capn2 | 0.0103996814 | 0.3097309 | 0.463 | 0.372 | 1 |
| Cnot2 | 0.0106449171 | 0.2985367 | 0.722 | 0.668 | 1 |
| Fam64a | 0.0125501686 | 0.2978154 | 0.741 | 0.680 | 1 |
| B2m | 0.0182648161 | 0.2661853 | 0.611 | 0.503 | 1 |
| Rrs1 | 0.0289127033 | 0.2541012 | 0.870 | 0.875 | 1 |
| Ung | 0.0794861941 | 0.2615626 | 0.704 | 0.703 | 1 |
psm.g <- subset(x = Emb8, subset = Mesp2 > 1 | Ripply2 > 1)
psm.g
An object of class Seurat 20210 features across 72 samples within 1 assay Active assay: RNA (20210 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap
emb8@meta.data$PSM <-
ifelse(
rownames(emb8@meta.data) %in% colnames(psm.g), "PSM", "Not")
#emb8@meta.data
DimPlot(object = emb8, group.by = 'PSM', pt.size = 1, cols = c("grey","blue"))
psmg <- SetIdent(emb8, value = emb8@meta.data$PSM)
Pmarkers <- FindMarkers(psmg, ident.1 = "PSM", only.pos = TRUE)
PSM_all <- Pmarkers[,0]
PSM_all <- head(PSM_all , n=20)
PSM_all
| Mesp2 |
|---|
| Pcdh8 |
| Ripply2 |
| Fgf18 |
| Mesp1 |
| Wfdc21 |
| Pcdh19 |
| Synm |
| Dll1 |
| Ptgds |
| Meox1 |
| Tcf15 |
| Foxc2 |
| Dll3 |
| Aldh1a2 |
| Foxc1 |
| Arg1 |
| Smoc1 |
| Kcnk3 |
| Aldoc |
FeaturePlot(Emb8, features = ('Tbx6'))
df <- subset(x = Emb8, subset = Tbx6 > 1 & T < 0.5 & Sox2 < 0.5)
df
An object of class Seurat 20210 features across 173 samples within 1 assay Active assay: RNA (20210 features, 2000 variable features) 2 dimensional reductions calculated: pca, umap
emb8@meta.data$df <-
ifelse(
rownames(emb8@meta.data) %in% colnames(df), "DF", "Not")
DimPlot(object = emb8, group.by = 'df', pt.size = 1, cols = c("blue","grey"))
diff <- SetIdent(emb8, value = emb8@meta.data$df)
markers <- FindMarkers(diff, ident.1 = "DF", only.pos = TRUE)
DF_all <- markers[,0]
DF_all <- head(DF_all , n=20)
DF_all
| Tbx6 |
|---|
| Pcdh19 |
| 3110004A20Rik |
| Dll1 |
| Dll3 |
| Hes7 |
| Rspo3 |
| Hoxb1 |
| Cdx1 |
| Cbr3 |
| Magi1 |
| Ifitm1 |
| Tceal6 |
| Kcnk3 |
| Lef1 |
| Aldh1a2 |
| Hoxb5os |
| Smoc1 |
| Cited1 |
| Rnf213 |